Get an attribute of this module as a Py.Object.t. This is useful to pass a Python function to another function.

Create a block diagonal matrix from provided arrays.

Given the inputs `A`, `B` and `C`, the output will have these arrays arranged on the diagonal::

[A, 0, 0], [0, B, 0], [0, 0, C]

Parameters ---------- A, B, C, ... : array_like, up to 2-D Input arrays. A 1-D array or array_like sequence of length `n` is treated as a 2-D array with shape ``(1,n)``.

Returns ------- D : ndarray Array with `A`, `B`, `C`, ... on the diagonal. `D` has the same dtype as `A`.

Notes ----- If all the input arrays are square, the output is known as a block diagonal matrix.

Empty sequences (i.e., array-likes of zero size) will not be ignored. Noteworthy, both and [] are treated as matrices with shape ``(1,0)``.

Examples -------- >>> from scipy.linalg import block_diag >>> A = [1, 0], ... [0, 1] >>> B = [3, 4, 5], ... [6, 7, 8] >>> C = [7] >>> P = np.zeros((2, 0), dtype='int32') >>> block_diag(A, B, C) array([1, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0], [0, 0, 3, 4, 5, 0], [0, 0, 6, 7, 8, 0], [0, 0, 0, 0, 0, 7]) >>> block_diag(A, P, B, C) array([1, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 3, 4, 5, 0], [0, 0, 6, 7, 8, 0], [0, 0, 0, 0, 0, 7]) >>> block_diag(1.0, 2, 3, [4, 5], [6, 7]) array([ 1., 0., 0., 0., 0.], [ 0., 2., 3., 0., 0.], [ 0., 0., 0., 4., 5.], [ 0., 0., 0., 6., 7.])

Converts complex eigenvalues ``w`` and eigenvectors ``v`` to real eigenvalues in a block diagonal form ``wr`` and the associated real eigenvectors ``vr``, such that::

vr @ wr = X @ vr

continues to hold, where ``X`` is the original array for which ``w`` and ``v`` are the eigenvalues and eigenvectors.

.. versionadded:: 1.1.0

Parameters ---------- w : (..., M) array_like Complex or real eigenvalues, an array or stack of arrays

Conjugate pairs must not be interleaved, else the wrong result will be produced. So ``1+1j, 1, 1-1j`` will give a correct result, but ``1+1j, 2+1j, 1-1j, 2-1j`` will not.

v : (..., M, M) array_like Complex or real eigenvectors, a square array or stack of square arrays.

Returns ------- wr : (..., M, M) ndarray Real diagonal block form of eigenvalues vr : (..., M, M) ndarray Real eigenvectors associated with ``wr``

See Also -------- eig : Eigenvalues and right eigenvectors for non-symmetric arrays rsf2csf : Convert real Schur form to complex Schur form

Notes ----- ``w``, ``v`` must be the eigenstructure for some *real* matrix ``X``. For example, obtained by ``w, v = scipy.linalg.eig(X)`` or ``w, v = numpy.linalg.eig(X)`` in which case ``X`` can also represent stacked arrays.

.. versionadded:: 1.1.0

Examples -------- >>> X = np.array([1, 2, 3], [0, 4, 5], [0, -5, 4]) >>> X array([ 1, 2, 3], [ 0, 4, 5], [ 0, -5, 4])

>>> from scipy import linalg >>> w, v = linalg.eig(X) >>> w array( 1.+0.j, 4.+5.j, 4.-5.j) >>> v array([ 1.00000+0.j , -0.01906-0.40016j, -0.01906+0.40016j], [ 0.00000+0.j , 0.00000-0.64788j, 0.00000+0.64788j], [ 0.00000+0.j , 0.64788+0.j , 0.64788-0.j ])

>>> wr, vr = linalg.cdf2rdf(w, v) >>> wr array([ 1., 0., 0.], [ 0., 4., 5.], [ 0., -5., 4.]) >>> vr array([ 1. , 0.40016, -0.01906], [ 0. , 0.64788, 0. ], [ 0. , 0. , 0.64788])

>>> vr @ wr array([ 1. , 1.69593, 1.9246 ], [ 0. , 2.59153, 3.23942], [ 0. , -3.23942, 2.59153]) >>> X @ vr array([ 1. , 1.69593, 1.9246 ], [ 0. , 2.59153, 3.23942], [ 0. , -3.23942, 2.59153])

Compute the Cholesky decomposition of a matrix, to use in cho_solve

Returns a matrix containing the Cholesky decomposition, ``A = L L*`` or ``A = U* U`` of a Hermitian positive-definite matrix `a`. The return value can be directly used as the first parameter to cho_solve.

.. warning:: The returned matrix also contains random data in the entries not used by the Cholesky decomposition. If you need to zero these entries, use the function `cholesky` instead.

Parameters ---------- a : (M, M) array_like Matrix to be decomposed lower : bool, optional Whether to compute the upper or lower triangular Cholesky factorization (Default: upper-triangular) overwrite_a : bool, optional Whether to overwrite data in a (may improve performance) check_finite : bool, optional Whether to check that the input matrix contains only finite numbers. Disabling may give a performance gain, but may result in problems (crashes, non-termination) if the inputs do contain infinities or NaNs.

Returns ------- c : (M, M) ndarray Matrix whose upper or lower triangle contains the Cholesky factor of `a`. Other parts of the matrix contain random data. lower : bool Flag indicating whether the factor is in the lower or upper triangle

Raises ------ LinAlgError Raised if decomposition fails.

See also -------- cho_solve : Solve a linear set equations using the Cholesky factorization of a matrix.

Examples -------- >>> from scipy.linalg import cho_factor >>> A = np.array([9, 3, 1, 5], [3, 7, 5, 1], [1, 5, 9, 2], [5, 1, 2, 6]) >>> c, low = cho_factor(A) >>> c array([3. , 1. , 0.33333333, 1.66666667], [3. , 2.44948974, 1.90515869, -0.27216553], [1. , 5. , 2.29330749, 0.8559528 ], [5. , 1. , 2. , 1.55418563]) >>> np.allclose(np.triu(c).T @ np. triu(c) - A, np.zeros((4, 4))) True

Solve the linear equations A x = b, given the Cholesky factorization of A.

Parameters ---------- (c, lower) : tuple, (array, bool) Cholesky factorization of a, as given by cho_factor b : array Right-hand side overwrite_b : bool, optional Whether to overwrite data in b (may improve performance) check_finite : bool, optional Whether to check that the input matrices contain only finite numbers. Disabling may give a performance gain, but may result in problems (crashes, non-termination) if the inputs do contain infinities or NaNs.

Returns ------- x : array The solution to the system A x = b

See also -------- cho_factor : Cholesky factorization of a matrix

Examples -------- >>> from scipy.linalg import cho_factor, cho_solve >>> A = np.array([9, 3, 1, 5], [3, 7, 5, 1], [1, 5, 9, 2], [5, 1, 2, 6]) >>> c, low = cho_factor(A) >>> x = cho_solve((c, low), 1, 1, 1, 1) >>> np.allclose(A @ x - 1, 1, 1, 1, np.zeros(4)) True

Solve the linear equations ``A x = b``, given the Cholesky factorization of the banded hermitian ``A``.

Parameters ---------- (cb, lower) : tuple, (ndarray, bool) `cb` is the Cholesky factorization of A, as given by cholesky_banded. `lower` must be the same value that was given to cholesky_banded. b : array_like Right-hand side overwrite_b : bool, optional If True, the function will overwrite the values in `b`. check_finite : bool, optional Whether to check that the input matrices contain only finite numbers. Disabling may give a performance gain, but may result in problems (crashes, non-termination) if the inputs do contain infinities or NaNs.

Returns ------- x : array The solution to the system A x = b

See also -------- cholesky_banded : Cholesky factorization of a banded matrix

Notes -----

.. versionadded:: 0.8.0

Examples -------- >>> from scipy.linalg import cholesky_banded, cho_solve_banded >>> Ab = np.array([0, 0, 1j, 2, 3j], [0, -1, -2, 3, 4], [9, 8, 7, 6, 9]) >>> A = np.diag(Ab0,2:, k=2) + np.diag(Ab1,1:, k=1) >>> A = A + A.conj().T + np.diag(Ab2, :) >>> c = cholesky_banded(Ab) >>> x = cho_solve_banded((c, False), np.ones(5)) >>> np.allclose(A @ x - np.ones(5), np.zeros(5)) True

Compute the Cholesky decomposition of a matrix.

Returns the Cholesky decomposition, :math:`A = L L^*` or :math:`A = U^* U` of a Hermitian positive-definite matrix A.

Parameters ---------- a : (M, M) array_like Matrix to be decomposed lower : bool, optional Whether to compute the upper- or lower-triangular Cholesky factorization. Default is upper-triangular. overwrite_a : bool, optional Whether to overwrite data in `a` (may improve performance). check_finite : bool, optional Whether to check that the input matrix contains only finite numbers. Disabling may give a performance gain, but may result in problems (crashes, non-termination) if the inputs do contain infinities or NaNs.

Returns ------- c : (M, M) ndarray Upper- or lower-triangular Cholesky factor of `a`.

Raises ------ LinAlgError : if decomposition fails.

Examples -------- >>> from scipy.linalg import cholesky >>> a = np.array([1,-2j],[2j,5]) >>> L = cholesky(a, lower=True) >>> L array([ 1.+0.j, 0.+0.j], [ 0.+2.j, 1.+0.j]) >>> L @ L.T.conj() array([ 1.+0.j, 0.-2.j], [ 0.+2.j, 5.+0.j])

Cholesky decompose a banded Hermitian positive-definite matrix

The matrix a is stored in ab either in lower-diagonal or upper- diagonal ordered form::

abu + i - j, j == ai,j (if upper form; i <= j) ab i - j, j == ai,j (if lower form; i >= j)

Example of ab (shape of a is (6,6), u=2)::

upper form: * * a02 a13 a24 a35 * a01 a12 a23 a34 a45 a00 a11 a22 a33 a44 a55

lower form: a00 a11 a22 a33 a44 a55 a10 a21 a32 a43 a54 * a20 a31 a42 a53 * *

Parameters ---------- ab : (u + 1, M) array_like Banded matrix overwrite_ab : bool, optional Discard data in ab (may enhance performance) lower : bool, optional Is the matrix in the lower form. (Default is upper form) check_finite : bool, optional Whether to check that the input matrix contains only finite numbers. Disabling may give a performance gain, but may result in problems (crashes, non-termination) if the inputs do contain infinities or NaNs.

Returns ------- c : (u + 1, M) ndarray Cholesky factorization of a, in the same banded format as ab

See also -------- cho_solve_banded : Solve a linear set equations, given the Cholesky factorization of a banded hermitian.

Examples -------- >>> from scipy.linalg import cholesky_banded >>> from numpy import allclose, zeros, diag >>> Ab = np.array([0, 0, 1j, 2, 3j], [0, -1, -2, 3, 4], [9, 8, 7, 6, 9]) >>> A = np.diag(Ab0,2:, k=2) + np.diag(Ab1,1:, k=1) >>> A = A + A.conj().T + np.diag(Ab2, :) >>> c = cholesky_banded(Ab) >>> C = np.diag(c0, 2:, k=2) + np.diag(c1, 1:, k=1) + np.diag(c2, :) >>> np.allclose(C.conj().T @ C - A, np.zeros((5, 5))) True

Construct a circulant matrix.

Parameters ---------- c : (N,) array_like 1-D array, the first column of the matrix.

Returns ------- A : (N, N) ndarray A circulant matrix whose first column is `c`.

See Also -------- toeplitz : Toeplitz matrix hankel : Hankel matrix solve_circulant : Solve a circulant system.

Notes ----- .. versionadded:: 0.8.0

Examples -------- >>> from scipy.linalg import circulant >>> circulant(1, 2, 3) array([1, 3, 2], [2, 1, 3], [3, 2, 1])

' Applies a Clarkson-Woodruff Transform/sketch to the input matrix.

Given an input_matrix ``A`` of size ``(n, d)``, compute a matrix ``A'`` of size (sketch_size, d) so that

.. math:: \|Ax\| \approx \|A'x\|

with high probability via the Clarkson-Woodruff Transform, otherwise known as the CountSketch matrix.

Parameters ---------- input_matrix: array_like Input matrix, of shape ``(n, d)``. sketch_size: int Number of rows for the sketch. seed : None or int or `numpy.random.RandomState` instance, optional This parameter defines the ``RandomState`` object to use for drawing random variates. If None (or ``np.random``), the global ``np.random`` state is used. If integer, it is used to seed the local ``RandomState`` instance. Default is None.

Returns ------- A' : array_like Sketch of the input matrix ``A``, of size ``(sketch_size, d)``.

Notes ----- To make the statement

.. math:: \|Ax\| \approx \|A'x\|

precise, observe the following result which is adapted from the proof of Theorem 14 of 2_ via Markov's Inequality. If we have a sketch size ``sketch_size=k`` which is at least

.. math:: k \geq \frac

\epsilon^2\delta

Then for any fixed vector ``x``,

.. math:: \|Ax\| = (1\pm\epsilon)\|A'x\|

with probability at least one minus delta.

This implementation takes advantage of sparsity: computing a sketch takes time proportional to ``A.nnz``. Data ``A`` which is in ``scipy.sparse.csc_matrix`` format gives the quickest computation time for sparse input.

>>> from scipy import linalg >>> from scipy import sparse >>> n_rows, n_columns, density, sketch_n_rows = 15000, 100, 0.01, 200 >>> A = sparse.rand(n_rows, n_columns, density=density, format='csc') >>> B = sparse.rand(n_rows, n_columns, density=density, format='csr') >>> C = sparse.rand(n_rows, n_columns, density=density, format='coo') >>> D = np.random.randn(n_rows, n_columns) >>> SA = linalg.clarkson_woodruff_transform(A, sketch_n_rows) # fastest >>> SB = linalg.clarkson_woodruff_transform(B, sketch_n_rows) # fast >>> SC = linalg.clarkson_woodruff_transform(C, sketch_n_rows) # slower >>> SD = linalg.clarkson_woodruff_transform(D, sketch_n_rows) # slowest

That said, this method does perform well on dense inputs, just slower on a relative scale.

Examples -------- Given a big dense matrix ``A``:

>>> from scipy import linalg >>> n_rows, n_columns, sketch_n_rows = 15000, 100, 200 >>> A = np.random.randn(n_rows, n_columns) >>> sketch = linalg.clarkson_woodruff_transform(A, sketch_n_rows) >>> sketch.shape (200, 100) >>> norm_A = np.linalg.norm(A) >>> norm_sketch = np.linalg.norm(sketch)

Now with high probability, the true norm ``norm_A`` is close to the sketched norm ``norm_sketch`` in absolute value.

Similarly, applying our sketch preserves the solution to a linear regression of :math:`\min \|Ax - b\|`.

>>> from scipy import linalg >>> n_rows, n_columns, sketch_n_rows = 15000, 100, 200 >>> A = np.random.randn(n_rows, n_columns) >>> b = np.random.randn(n_rows) >>> x = np.linalg.lstsq(A, b, rcond=None) >>> Ab = np.hstack((A, b.reshape(-1,1))) >>> SAb = linalg.clarkson_woodruff_transform(Ab, sketch_n_rows) >>> SA, Sb = SAb:,:-1, SAb:,-1 >>> x_sketched = np.linalg.lstsq(SA, Sb, rcond=None)

As with the matrix norm example, ``np.linalg.norm(A @ x - b)`` is close to ``np.linalg.norm(A @ x_sketched - b)`` with high probability.

References ---------- .. 1 Kenneth L. Clarkson and David P. Woodruff. Low rank approximation and regression in input sparsity time. In STOC, 2013.

.. 2 David P. Woodruff. Sketching as a tool for numerical linear algebra. In Foundations and Trends in Theoretical Computer Science, 2014.

Create a companion matrix.

Create the companion matrix 1_ associated with the polynomial whose coefficients are given in `a`.

Parameters ---------- a : (N,) array_like 1-D array of polynomial coefficients. The length of `a` must be at least two, and ``a0`` must not be zero.

Returns ------- c : (N-1, N-1) ndarray The first row of `c` is ``-a1:/a0``, and the first sub-diagonal is all ones. The data-type of the array is the same as the data-type of ``1.0*a0``.

Raises ------ ValueError If any of the following are true: a) ``a.ndim != 1``; b) ``a.size < 2``; c) ``a0 == 0``.

Notes ----- .. versionadded:: 0.8.0

References ---------- .. 1 R. A. Horn & C. R. Johnson, *Matrix Analysis*. Cambridge, UK: Cambridge University Press, 1999, pp. 146-7.

Examples -------- >>> from scipy.linalg import companion >>> companion(1, -10, 31, -30) array([ 10., -31., 30.], [ 1., 0., 0.], [ 0., 1., 0.])

Construct a convolution matrix.

Constructs the Toeplitz matrix representing one-dimensional convolution 1_. See the notes below for details.

Parameters ---------- a : (m,) array_like The 1-D array to convolve. n : int The number of columns in the resulting matrix. It gives the length of the input to be convolved with `a`. This is analogous to the length of `v` in ``numpy.convolve(a, v)``. mode : str This is analogous to `mode` in ``numpy.convolve(v, a, mode)``. It must be one of ('full', 'valid', 'same'). See below for how `mode` determines the shape of the result.

Returns ------- A : (k, n) ndarray The convolution matrix whose row count `k` depends on `mode`::

======= ========================= mode k ======= ========================= 'full' m + n -1 'same' max(m, n) 'valid' max(m, n) - min(m, n) + 1 ======= =========================

See Also -------- toeplitz : Toeplitz matrix

Notes ----- The code::

A = convolution_matrix(a, n, mode)

creates a Toeplitz matrix `A` such that ``A @ v`` is equivalent to using ``convolve(a, v, mode)``. The returned array always has `n` columns. The number of rows depends on the specified `mode`, as explained above.

In the default 'full' mode, the entries of `A` are given by::

Ai, j == (ai-j if (0 <= (i-j) < m) else 0)

where ``m = len(a)``. Suppose, for example, the input array is ``x, y, z``. The convolution matrix has the form::

x, 0, 0, ..., 0, 0 y, x, 0, ..., 0, 0 z, y, x, ..., 0, 0 ... 0, 0, 0, ..., x, 0 0, 0, 0, ..., y, x 0, 0, 0, ..., z, y 0, 0, 0, ..., 0, z

In 'valid' mode, the entries of `A` are given by::

Ai, j == (ai-j+m-1 if (0 <= (i-j+m-1) < m) else 0)

This corresponds to a matrix whose rows are the subset of those from the 'full' case where all the coefficients in `a` are contained in the row. For input ``x, y, z``, this array looks like::

z, y, x, 0, 0, ..., 0, 0, 0 0, z, y, x, 0, ..., 0, 0, 0 0, 0, z, y, x, ..., 0, 0, 0 ... 0, 0, 0, 0, 0, ..., x, 0, 0 0, 0, 0, 0, 0, ..., y, x, 0 0, 0, 0, 0, 0, ..., z, y, x

In the 'same' mode, the entries of `A` are given by::

d = (m - 1) // 2 Ai, j == (ai-j+d if (0 <= (i-j+d) < m) else 0)

The typical application of the 'same' mode is when one has a signal of length `n` (with `n` greater than ``len(a)``), and the desired output is a filtered signal that is still of length `n`.

For input ``x, y, z``, this array looks like::

y, x, 0, 0, ..., 0, 0, 0 z, y, x, 0, ..., 0, 0, 0 0, z, y, x, ..., 0, 0, 0 0, 0, z, y, ..., 0, 0, 0 ... 0, 0, 0, 0, ..., y, x, 0 0, 0, 0, 0, ..., z, y, x 0, 0, 0, 0, ..., 0, z, y

.. versionadded:: 1.5.0

References ---------- .. 1 'Convolution', https://en.wikipedia.org/wiki/Convolution

Examples -------- >>> from scipy.linalg import convolution_matrix >>> A = convolution_matrix(-1, 4, -2, 5, mode='same') >>> A array([ 4, -1, 0, 0, 0], [-2, 4, -1, 0, 0], [ 0, -2, 4, -1, 0], [ 0, 0, -2, 4, -1], [ 0, 0, 0, -2, 4])

Compare multiplication by `A` with the use of `numpy.convolve`.

>>> x = np.array(1, 2, 0, -3, 0.5) >>> A @ x array( 2. , 6. , -1. , -12.5, 8. )

Verify that ``A @ x`` produced the same result as applying the convolution function.

>>> np.convolve(-1, 4, -2, x, mode='same') array( 2. , 6. , -1. , -12.5, 8. )

For comparison to the case ``mode='same'`` shown above, here are the matrices produced by ``mode='full'`` and ``mode='valid'`` for the same coefficients and size.

>>> convolution_matrix(-1, 4, -2, 5, mode='full') array([-1, 0, 0, 0, 0], [ 4, -1, 0, 0, 0], [-2, 4, -1, 0, 0], [ 0, -2, 4, -1, 0], [ 0, 0, -2, 4, -1], [ 0, 0, 0, -2, 4], [ 0, 0, 0, 0, -2])

>>> convolution_matrix(-1, 4, -2, 5, mode='valid') array([-2, 4, -1, 0, 0], [ 0, -2, 4, -1, 0], [ 0, 0, -2, 4, -1])

Compute the hyperbolic matrix cosine.

This routine uses expm to compute the matrix exponentials.

Parameters ---------- A : (N, N) array_like Input array.

Returns ------- coshm : (N, N) ndarray Hyperbolic matrix cosine of `A`

Examples -------- >>> from scipy.linalg import tanhm, sinhm, coshm >>> a = np.array([1.0, 3.0], [1.0, 4.0]) >>> c = coshm(a) >>> c array([ 11.24592233, 38.76236492], [ 12.92078831, 50.00828725])

Verify tanhm(a) = sinhm(a).dot(inv(coshm(a)))

>>> t = tanhm(a) >>> s = sinhm(a) >>> t - s.dot(np.linalg.inv(c)) array([ 2.72004641e-15, 4.55191440e-15], [ 0.00000000e+00, -5.55111512e-16])

Compute the matrix cosine.

This routine uses expm to compute the matrix exponentials.

Parameters ---------- A : (N, N) array_like Input array

Returns ------- cosm : (N, N) ndarray Matrix cosine of A

Examples -------- >>> from scipy.linalg import expm, sinm, cosm

Euler's identity (exp(i*theta) = cos(theta) + i*sin(theta)) applied to a matrix:

>>> a = np.array([1.0, 2.0], [-1.0, 3.0]) >>> expm(1j*a) array([ 0.42645930+1.89217551j, -2.13721484-0.97811252j], [ 1.06860742+0.48905626j, -1.71075555+0.91406299j]) >>> cosm(a) + 1j*sinm(a) array([ 0.42645930+1.89217551j, -2.13721484-0.97811252j], [ 1.06860742+0.48905626j, -1.71075555+0.91406299j])

Compute the cosine-sine (CS) decomposition of an orthogonal/unitary matrix.

X is an ``(m, m)`` orthogonal/unitary matrix, partitioned as the following where upper left block has the shape of ``(p, q)``::

┌ ┐ │ I 0 0 │ 0 0 0 │ ┌ ┐ ┌ ┐│ 0 C 0 │ 0 -S 0 │┌ ┐* │ X11 │ X12 │ │ U1 │ ││ 0 0 0 │ 0 0 -I ││ V1 │ │ │ ────┼──── │ = │────┼────││─────────┼─────────││────┼────│ │ X21 │ X22 │ │ │ U2 ││ 0 0 0 │ I 0 0 ││ │ V2 │ └ ┘ └ ┘│ 0 S 0 │ 0 C 0 │└ ┘ │ 0 0 I │ 0 0 0 │ └ ┘

``U1``, ``U2``, ``V1``, ``V2`` are square orthogonal/unitary matrices of dimensions ``(p,p)``, ``(m-p,m-p)``, ``(q,q)``, and ``(m-q,m-q)`` respectively, and ``C`` and ``S`` are ``(r, r)`` nonnegative diagonal matrices satisfying ``C^2 + S^2 = I`` where ``r = min(p, m-p, q, m-q)``.

Moreover, the rank of the identity matrices are ``min(p, q) - r``, ``min(p, m - q) - r``, ``min(m - p, q) - r``, and ``min(m - p, m - q) - r`` respectively.

X can be supplied either by itself and block specifications p, q or its subblocks in an iterable from which the shapes would be derived. See the examples below.

Parameters ---------- X : array_like, iterable complex unitary or real orthogonal matrix to be decomposed, or iterable of subblocks ``X11``, ``X12``, ``X21``, ``X22``, when ``p``, ``q`` are omitted. p : int, optional Number of rows of the upper left block ``X11``, used only when X is given as an array. q : int, optional Number of columns of the upper left block ``X11``, used only when X is given as an array. separate : bool, optional if ``True``, the low level components are returned instead of the matrix factors, i.e. ``(u1,u2)``, ``theta``, ``(v1h,v2h)`` instead of ``u``, ``cs``, ``vh``. swap_sign : bool, optional if ``True``, the ``-S``, ``-I`` block will be the bottom left, otherwise (by default) they will be in the upper right block. compute_u : bool, optional if ``False``, ``u`` won't be computed and an empty array is returned. compute_vh : bool, optional if ``False``, ``vh`` won't be computed and an empty array is returned.

Returns ------- u : ndarray When ``compute_u=True``, contains the block diagonal orthogonal/unitary matrix consisting of the blocks ``U1`` (``p`` x ``p``) and ``U2`` (``m-p`` x ``m-p``) orthogonal/unitary matrices. If ``separate=True``, this contains the tuple of ``(U1, U2)``. cs : ndarray The cosine-sine factor with the structure described above. If ``separate=True``, this contains the ``theta`` array containing the angles in radians. vh : ndarray When ``compute_vh=True`, contains the block diagonal orthogonal/unitary matrix consisting of the blocks ``V1H`` (``q`` x ``q``) and ``V2H`` (``m-q`` x ``m-q``) orthogonal/unitary matrices. If ``separate=True``, this contains the tuple of ``(V1H, V2H)``.

Examples -------- >>> from scipy.linalg import cossin >>> from scipy.stats import unitary_group >>> x = unitary_group.rvs(4) >>> u, cs, vdh = cossin(x, p=2, q=2) >>> np.allclose(x, u @ cs @ vdh) True

Same can be entered via subblocks without the need of ``p`` and ``q``. Also let's skip the computation of ``u``

>>> ue, cs, vdh = cossin((x:2, :2, x:2, 2:, x2:, :2, x2:, 2:), ... compute_u=False) >>> print(ue) >>> np.allclose(x, u @ cs @ vdh) True

References ---------- .. 1 : Brian D. Sutton. Computing the complete CS decomposition. Numer. Algorithms, 50(1):33-65, 2009.

Compute the determinant of a matrix

The determinant of a square matrix is a value derived arithmetically from the coefficients of the matrix.

The determinant for a 3x3 matrix, for example, is computed as follows::

a b c d e f = A g h i

det(A) = a*e*i + b*f*g + c*d*h - c*e*g - b*d*i - a*f*h

Parameters ---------- a : (M, M) array_like A square matrix. overwrite_a : bool, optional Allow overwriting data in a (may enhance performance). check_finite : bool, optional Whether to check that the input matrix contains only finite numbers. Disabling may give a performance gain, but may result in problems (crashes, non-termination) if the inputs do contain infinities or NaNs.

Returns ------- det : float or complex Determinant of `a`.

Notes ----- The determinant is computed via LU factorization, LAPACK routine z/dgetrf.

Examples -------- >>> from scipy import linalg >>> a = np.array([1,2,3], [4,5,6], [7,8,9]) >>> linalg.det(a) 0.0 >>> a = np.array([0,2,3], [4,5,6], [7,8,9]) >>> linalg.det(a) 3.0

Discrete Fourier transform matrix.

Create the matrix that computes the discrete Fourier transform of a sequence 1_. The nth primitive root of unity used to generate the matrix is exp(-2*pi*i/n), where i = sqrt(-1).

Parameters ---------- n : int Size the matrix to create. scale : str, optional Must be None, 'sqrtn', or 'n'. If `scale` is 'sqrtn', the matrix is divided by `sqrt(n)`. If `scale` is 'n', the matrix is divided by `n`. If `scale` is None (the default), the matrix is not normalized, and the return value is simply the Vandermonde matrix of the roots of unity.

Returns ------- m : (n, n) ndarray The DFT matrix.

Notes ----- When `scale` is None, multiplying a vector by the matrix returned by `dft` is mathematically equivalent to (but much less efficient than) the calculation performed by `scipy.fft.fft`.

.. versionadded:: 0.14.0

References ---------- .. 1 'DFT matrix', https://en.wikipedia.org/wiki/DFT_matrix

Examples -------- >>> from scipy.linalg import dft >>> np.set_printoptions(precision=2, suppress=True) # for compact output >>> m = dft(5) >>> m array([ 1. +0.j , 1. +0.j , 1. +0.j , 1. +0.j , 1. +0.j ], [ 1. +0.j , 0.31-0.95j, -0.81-0.59j, -0.81+0.59j, 0.31+0.95j], [ 1. +0.j , -0.81-0.59j, 0.31+0.95j, 0.31-0.95j, -0.81+0.59j], [ 1. +0.j , -0.81+0.59j, 0.31-0.95j, 0.31+0.95j, -0.81-0.59j], [ 1. +0.j , 0.31+0.95j, -0.81+0.59j, -0.81-0.59j, 0.31-0.95j]) >>> x = np.array(1, 2, 3, 0, 3) >>> m @ x # Compute the DFT of x array( 9. +0.j , 0.12-0.81j, -2.12+3.44j, -2.12-3.44j, 0.12+0.81j)

Verify that ``m @ x`` is the same as ``fft(x)``.

>>> from scipy.fft import fft >>> fft(x) # Same result as m @ x array( 9. +0.j , 0.12-0.81j, -2.12+3.44j, -2.12-3.44j, 0.12+0.81j)

Construct the sigma matrix in SVD from singular values and size M, N.

Parameters ---------- s : (M,) or (N,) array_like Singular values M : int Size of the matrix whose singular values are `s`. N : int Size of the matrix whose singular values are `s`.

Returns ------- S : (M, N) ndarray The S-matrix in the singular value decomposition

See Also -------- svd : Singular value decomposition of a matrix svdvals : Compute singular values of a matrix.

Examples -------- >>> from scipy.linalg import diagsvd >>> vals = np.array(1, 2, 3) # The array representing the computed svd >>> diagsvd(vals, 3, 4) array([1, 0, 0, 0], [0, 2, 0, 0], [0, 0, 3, 0]) >>> diagsvd(vals, 4, 3) array([1, 0, 0], [0, 2, 0], [0, 0, 3], [0, 0, 0])

Solve an ordinary or generalized eigenvalue problem of a square matrix.

Find eigenvalues w and right or left eigenvectors of a general matrix::

a vr:,i = wi b vr:,i a.H vl:,i = wi.conj() b.H vl:,i

where ``.H`` is the Hermitian conjugation.

Parameters ---------- a : (M, M) array_like A complex or real matrix whose eigenvalues and eigenvectors will be computed. b : (M, M) array_like, optional Right-hand side matrix in a generalized eigenvalue problem. Default is None, identity matrix is assumed. left : bool, optional Whether to calculate and return left eigenvectors. Default is False. right : bool, optional Whether to calculate and return right eigenvectors. Default is True. overwrite_a : bool, optional Whether to overwrite `a`; may improve performance. Default is False. overwrite_b : bool, optional Whether to overwrite `b`; may improve performance. Default is False. check_finite : bool, optional Whether to check that the input matrices contain only finite numbers. Disabling may give a performance gain, but may result in problems (crashes, non-termination) if the inputs do contain infinities or NaNs. homogeneous_eigvals : bool, optional If True, return the eigenvalues in homogeneous coordinates. In this case ``w`` is a (2, M) array so that::

w1,i a vr:,i = w0,i b vr:,i

Default is False.

Returns ------- w : (M,) or (2, M) double or complex ndarray The eigenvalues, each repeated according to its multiplicity. The shape is (M,) unless ``homogeneous_eigvals=True``. vl : (M, M) double or complex ndarray The normalized left eigenvector corresponding to the eigenvalue ``wi`` is the column vl:,i. Only returned if ``left=True``. vr : (M, M) double or complex ndarray The normalized right eigenvector corresponding to the eigenvalue ``wi`` is the column ``vr:,i``. Only returned if ``right=True``.

Raises ------ LinAlgError If eigenvalue computation does not converge.

See Also -------- eigvals : eigenvalues of general arrays eigh : Eigenvalues and right eigenvectors for symmetric/Hermitian arrays. eig_banded : eigenvalues and right eigenvectors for symmetric/Hermitian band matrices eigh_tridiagonal : eigenvalues and right eiegenvectors for symmetric/Hermitian tridiagonal matrices

Examples -------- >>> from scipy import linalg >>> a = np.array([0., -1.], [1., 0.]) >>> linalg.eigvals(a) array(0.+1.j, 0.-1.j)

>>> b = np.array([0., 1.], [1., 1.]) >>> linalg.eigvals(a, b) array( 1.+0.j, -1.+0.j)

>>> a = np.array([3., 0., 0.], [0., 8., 0.], [0., 0., 7.]) >>> linalg.eigvals(a, homogeneous_eigvals=True) array([3.+0.j, 8.+0.j, 7.+0.j], [1.+0.j, 1.+0.j, 1.+0.j])

>>> a = np.array([0., -1.], [1., 0.]) >>> linalg.eigvals(a) == linalg.eig(a)0 array( True, True) >>> linalg.eig(a, left=True, right=False)1 # normalized left eigenvector array([-0.70710678+0.j , -0.70710678-0.j ], [-0. +0.70710678j, -0. -0.70710678j]) >>> linalg.eig(a, left=False, right=True)1 # normalized right eigenvector array([0.70710678+0.j , 0.70710678-0.j ], [0. -0.70710678j, 0. +0.70710678j])

Solve real symmetric or complex Hermitian band matrix eigenvalue problem.

Find eigenvalues w and optionally right eigenvectors v of a::

a v:,i = wi v:,i v.H v = identity

The matrix a is stored in a_band either in lower diagonal or upper diagonal ordered form:

a_bandu + i - j, j == ai,j (if upper form; i <= j) a_band i - j, j == ai,j (if lower form; i >= j)

where u is the number of bands above the diagonal.

Example of a_band (shape of a is (6,6), u=2)::

upper form: * * a02 a13 a24 a35 * a01 a12 a23 a34 a45 a00 a11 a22 a33 a44 a55

lower form: a00 a11 a22 a33 a44 a55 a10 a21 a32 a43 a54 * a20 a31 a42 a53 * *

Cells marked with * are not used.

Parameters ---------- a_band : (u+1, M) array_like The bands of the M by M matrix a. lower : bool, optional Is the matrix in the lower form. (Default is upper form) eigvals_only : bool, optional Compute only the eigenvalues and no eigenvectors. (Default: calculate also eigenvectors) overwrite_a_band : bool, optional Discard data in a_band (may enhance performance) select : 'a', 'v', 'i', optional Which eigenvalues to calculate

====== ======================================== select calculated ====== ======================================== 'a' All eigenvalues 'v' Eigenvalues in the interval (min, max] 'i' Eigenvalues with indices min <= i <= max ====== ======================================== select_range : (min, max), optional Range of selected eigenvalues max_ev : int, optional For select=='v', maximum number of eigenvalues expected. For other values of select, has no meaning.

In doubt, leave this parameter untouched.

check_finite : bool, optional Whether to check that the input matrix contains only finite numbers. Disabling may give a performance gain, but may result in problems (crashes, non-termination) if the inputs do contain infinities or NaNs.

Returns ------- w : (M,) ndarray The eigenvalues, in ascending order, each repeated according to its multiplicity. v : (M, M) float or complex ndarray The normalized eigenvector corresponding to the eigenvalue wi is the column v:,i.

Raises ------ LinAlgError If eigenvalue computation does not converge.

See Also -------- eigvals_banded : eigenvalues for symmetric/Hermitian band matrices eig : eigenvalues and right eigenvectors of general arrays. eigh : eigenvalues and right eigenvectors for symmetric/Hermitian arrays eigh_tridiagonal : eigenvalues and right eiegenvectors for symmetric/Hermitian tridiagonal matrices

Examples -------- >>> from scipy.linalg import eig_banded >>> A = np.array([1, 5, 2, 0], [5, 2, 5, 2], [2, 5, 3, 5], [0, 2, 5, 4]) >>> Ab = np.array([1, 2, 3, 4], [5, 5, 5, 0], [2, 2, 0, 0]) >>> w, v = eig_banded(Ab, lower=True) >>> np.allclose(A @ v - v @ np.diag(w), np.zeros((4, 4))) True >>> w = eig_banded(Ab, lower=True, eigvals_only=True) >>> w array(-4.26200532, -2.22987175, 3.95222349, 12.53965359)

Request only the eigenvalues between ``-3, 4``

>>> w, v = eig_banded(Ab, lower=True, select='v', select_range=-3, 4) >>> w array(-2.22987175, 3.95222349)

Solve a standard or generalized eigenvalue problem for a complex Hermitian or real symmetric matrix.

Find eigenvalues array ``w`` and optionally eigenvectors array ``v`` of array ``a``, where ``b`` is positive definite such that for every eigenvalue λ (i-th entry of w) and its eigenvector ``vi`` (i-th column of ``v``) satisfies::

a @ vi = λ * b @ vi vi.conj().T @ a @ vi = λ vi.conj().T @ b @ vi = 1

In the standard problem, ``b`` is assumed to be the identity matrix.

Parameters ---------- a : (M, M) array_like A complex Hermitian or real symmetric matrix whose eigenvalues and eigenvectors will be computed. b : (M, M) array_like, optional A complex Hermitian or real symmetric definite positive matrix in. If omitted, identity matrix is assumed. lower : bool, optional Whether the pertinent array data is taken from the lower or upper triangle of ``a`` and, if applicable, ``b``. (Default: lower) eigvals_only : bool, optional Whether to calculate only eigenvalues and no eigenvectors. (Default: both are calculated) subset_by_index : iterable, optional If provided, this two-element iterable defines the start and the end indices of the desired eigenvalues (ascending order and 0-indexed). To return only the second smallest to fifth smallest eigenvalues, ``1, 4`` is used. ``n-3, n-1`` returns the largest three. Only available with 'evr', 'evx', and 'gvx' drivers. The entries are directly converted to integers via ``int()``. subset_by_value : iterable, optional If provided, this two-element iterable defines the half-open interval ``(a, b]`` that, if any, only the eigenvalues between these values are returned. Only available with 'evr', 'evx', and 'gvx' drivers. Use ``np.inf`` for the unconstrained ends. driver: str, optional Defines which LAPACK driver should be used. Valid options are 'ev', 'evd', 'evr', 'evx' for standard problems and 'gv', 'gvd', 'gvx' for generalized (where b is not None) problems. See the Notes section. type : int, optional For the generalized problems, this keyword specifies the problem type to be solved for ``w`` and ``v`` (only takes 1, 2, 3 as possible inputs)::

1 => a @ v = w @ b @ v 2 => a @ b @ v = w @ v 3 => b @ a @ v = w @ v

This keyword is ignored for standard problems. overwrite_a : bool, optional Whether to overwrite data in ``a`` (may improve performance). Default is False. overwrite_b : bool, optional Whether to overwrite data in ``b`` (may improve performance). Default is False. check_finite : bool, optional Whether to check that the input matrices contain only finite numbers. Disabling may give a performance gain, but may result in problems (crashes, non-termination) if the inputs do contain infinities or NaNs. turbo : bool, optional *Deprecated since v1.5.0, use ``driver=gvd`` keyword instead*. Use divide and conquer algorithm (faster but expensive in memory, only for generalized eigenvalue problem and if full set of eigenvalues are requested.). Has no significant effect if eigenvectors are not requested. eigvals : tuple (lo, hi), optional *Deprecated since v1.5.0, use ``subset_by_index`` keyword instead*. Indexes of the smallest and largest (in ascending order) eigenvalues and corresponding eigenvectors to be returned: 0 <= lo <= hi <= M-1. If omitted, all eigenvalues and eigenvectors are returned.

Returns ------- w : (N,) ndarray The N (1<=N<=M) selected eigenvalues, in ascending order, each repeated according to its multiplicity. v : (M, N) ndarray (if ``eigvals_only == False``)

Raises ------ LinAlgError If eigenvalue computation does not converge, an error occurred, or b matrix is not definite positive. Note that if input matrices are not symmetric or Hermitian, no error will be reported but results will be wrong.

See Also -------- eigvalsh : eigenvalues of symmetric or Hermitian arrays eig : eigenvalues and right eigenvectors for non-symmetric arrays eigh_tridiagonal : eigenvalues and right eiegenvectors for symmetric/Hermitian tridiagonal matrices

Notes ----- This function does not check the input array for being hermitian/symmetric in order to allow for representing arrays with only their upper/lower triangular parts. Also, note that even though not taken into account, finiteness check applies to the whole array and unaffected by 'lower' keyword.

This function uses LAPACK drivers for computations in all possible keyword combinations, prefixed with ``sy`` if arrays are real and ``he`` if complex, e.g., a float array with 'evr' driver is solved via 'syevr', complex arrays with 'gvx' driver problem is solved via 'hegvx' etc.

As a brief summary, the slowest and the most robust driver is the classical ``<sy/he>ev`` which uses symmetric QR. ``<sy/he>evr`` is seen as the optimal choice for the most general cases. However, there are certain occassions that ``<sy/he>evd`` computes faster at the expense of more memory usage. ``<sy/he>evx``, while still being faster than ``<sy/he>ev``, often performs worse than the rest except when very few eigenvalues are requested for large arrays though there is still no performance guarantee.

For the generalized problem, normalization with respoect to the given type argument::

type 1 and 3 : v.conj().T @ a @ v = w type 2 : inv(v).conj().T @ a @ inv(v) = w

type 1 or 2 : v.conj().T @ b @ v = I type 3 : v.conj().T @ inv(b) @ v = I

Examples -------- >>> from scipy.linalg import eigh >>> A = np.array([6, 3, 1, 5], [3, 0, 5, 1], [1, 5, 6, 2], [5, 1, 2, 2]) >>> w, v = eigh(A) >>> np.allclose(A @ v - v @ np.diag(w), np.zeros((4, 4))) True

Request only the eigenvalues

>>> w = eigh(A, eigvals_only=True)

Request eigenvalues that are less than 10.

>>> A = np.array([34, -4, -10, -7, 2], ... [-4, 7, 2, 12, 0], ... [-10, 2, 44, 2, -19], ... [-7, 12, 2, 79, -34], ... [2, 0, -19, -34, 29]) >>> eigh(A, eigvals_only=True, subset_by_value=-np.inf, 10) array(6.69199443e-07, 9.11938152e+00)

Request the largest second eigenvalue and its eigenvector

>>> w, v = eigh(A, subset_by_index=1, 1) >>> w array(9.11938152) >>> v.shape # only a single column is returned (5, 1)

Solve eigenvalue problem for a real symmetric tridiagonal matrix.

Find eigenvalues `w` and optionally right eigenvectors `v` of ``a``::

a v:,i = wi v:,i v.H v = identity

For a real symmetric matrix ``a`` with diagonal elements `d` and off-diagonal elements `e`.

Parameters ---------- d : ndarray, shape (ndim,) The diagonal elements of the array. e : ndarray, shape (ndim-1,) The off-diagonal elements of the array. select : 'a', 'v', 'i', optional Which eigenvalues to calculate

====== ======================================== select calculated ====== ======================================== 'a' All eigenvalues 'v' Eigenvalues in the interval (min, max] 'i' Eigenvalues with indices min <= i <= max ====== ======================================== select_range : (min, max), optional Range of selected eigenvalues check_finite : bool, optional Whether to check that the input matrix contains only finite numbers. Disabling may give a performance gain, but may result in problems (crashes, non-termination) if the inputs do contain infinities or NaNs. tol : float The absolute tolerance to which each eigenvalue is required (only used when 'stebz' is the `lapack_driver`). An eigenvalue (or cluster) is considered to have converged if it lies in an interval of this width. If <= 0. (default), the value ``eps*|a|`` is used where eps is the machine precision, and ``|a|`` is the 1-norm of the matrix ``a``. lapack_driver : str LAPACK function to use, can be 'auto', 'stemr', 'stebz', 'sterf', or 'stev'. When 'auto' (default), it will use 'stemr' if ``select='a'`` and 'stebz' otherwise. When 'stebz' is used to find the eigenvalues and ``eigvals_only=False``, then a second LAPACK call (to ``?STEIN``) is used to find the corresponding eigenvectors. 'sterf' can only be used when ``eigvals_only=True`` and ``select='a'``. 'stev' can only be used when ``select='a'``.

Returns ------- w : (M,) ndarray The eigenvalues, in ascending order, each repeated according to its multiplicity. v : (M, M) ndarray The normalized eigenvector corresponding to the eigenvalue ``wi`` is the column ``v:,i``.

Raises ------ LinAlgError If eigenvalue computation does not converge.

See Also -------- eigvalsh_tridiagonal : eigenvalues of symmetric/Hermitian tridiagonal matrices eig : eigenvalues and right eigenvectors for non-symmetric arrays eigh : eigenvalues and right eigenvectors for symmetric/Hermitian arrays eig_banded : eigenvalues and right eigenvectors for symmetric/Hermitian band matrices

Notes ----- This function makes use of LAPACK ``S/DSTEMR`` routines.

Examples -------- >>> from scipy.linalg import eigh_tridiagonal >>> d = 3*np.ones(4) >>> e = -1*np.ones(3) >>> w, v = eigh_tridiagonal(d, e) >>> A = np.diag(d) + np.diag(e, k=1) + np.diag(e, k=-1) >>> np.allclose(A @ v - v @ np.diag(w), np.zeros((4, 4))) True

Compute eigenvalues from an ordinary or generalized eigenvalue problem.

Find eigenvalues of a general matrix::

a vr:,i = wi b vr:,i

Parameters ---------- a : (M, M) array_like A complex or real matrix whose eigenvalues and eigenvectors will be computed. b : (M, M) array_like, optional Right-hand side matrix in a generalized eigenvalue problem. If omitted, identity matrix is assumed. overwrite_a : bool, optional Whether to overwrite data in a (may improve performance) check_finite : bool, optional Whether to check that the input matrices contain only finite numbers. Disabling may give a performance gain, but may result in problems (crashes, non-termination) if the inputs do contain infinities or NaNs. homogeneous_eigvals : bool, optional If True, return the eigenvalues in homogeneous coordinates. In this case ``w`` is a (2, M) array so that::

w1,i a vr:,i = w0,i b vr:,i

Default is False.

Returns ------- w : (M,) or (2, M) double or complex ndarray The eigenvalues, each repeated according to its multiplicity but not in any specific order. The shape is (M,) unless ``homogeneous_eigvals=True``.

Raises ------ LinAlgError If eigenvalue computation does not converge

See Also -------- eig : eigenvalues and right eigenvectors of general arrays. eigvalsh : eigenvalues of symmetric or Hermitian arrays eigvals_banded : eigenvalues for symmetric/Hermitian band matrices eigvalsh_tridiagonal : eigenvalues of symmetric/Hermitian tridiagonal matrices

Examples -------- >>> from scipy import linalg >>> a = np.array([0., -1.], [1., 0.]) >>> linalg.eigvals(a) array(0.+1.j, 0.-1.j)

>>> b = np.array([0., 1.], [1., 1.]) >>> linalg.eigvals(a, b) array( 1.+0.j, -1.+0.j)

>>> a = np.array([3., 0., 0.], [0., 8., 0.], [0., 0., 7.]) >>> linalg.eigvals(a, homogeneous_eigvals=True) array([3.+0.j, 8.+0.j, 7.+0.j], [1.+0.j, 1.+0.j, 1.+0.j])

Solve real symmetric or complex Hermitian band matrix eigenvalue problem.

Find eigenvalues w of a::

a v:,i = wi v:,i v.H v = identity

The matrix a is stored in a_band either in lower diagonal or upper diagonal ordered form:

a_bandu + i - j, j == ai,j (if upper form; i <= j) a_band i - j, j == ai,j (if lower form; i >= j)

where u is the number of bands above the diagonal.

Example of a_band (shape of a is (6,6), u=2)::

upper form: * * a02 a13 a24 a35 * a01 a12 a23 a34 a45 a00 a11 a22 a33 a44 a55

lower form: a00 a11 a22 a33 a44 a55 a10 a21 a32 a43 a54 * a20 a31 a42 a53 * *

Cells marked with * are not used.

Parameters ---------- a_band : (u+1, M) array_like The bands of the M by M matrix a. lower : bool, optional Is the matrix in the lower form. (Default is upper form) overwrite_a_band : bool, optional Discard data in a_band (may enhance performance) select : 'a', 'v', 'i', optional Which eigenvalues to calculate

====== ======================================== select calculated ====== ======================================== 'a' All eigenvalues 'v' Eigenvalues in the interval (min, max] 'i' Eigenvalues with indices min <= i <= max ====== ======================================== select_range : (min, max), optional Range of selected eigenvalues check_finite : bool, optional Whether to check that the input matrix contains only finite numbers. Disabling may give a performance gain, but may result in problems (crashes, non-termination) if the inputs do contain infinities or NaNs.

Returns ------- w : (M,) ndarray The eigenvalues, in ascending order, each repeated according to its multiplicity.

Raises ------ LinAlgError If eigenvalue computation does not converge.

See Also -------- eig_banded : eigenvalues and right eigenvectors for symmetric/Hermitian band matrices eigvalsh_tridiagonal : eigenvalues of symmetric/Hermitian tridiagonal matrices eigvals : eigenvalues of general arrays eigh : eigenvalues and right eigenvectors for symmetric/Hermitian arrays eig : eigenvalues and right eigenvectors for non-symmetric arrays

Examples -------- >>> from scipy.linalg import eigvals_banded >>> A = np.array([1, 5, 2, 0], [5, 2, 5, 2], [2, 5, 3, 5], [0, 2, 5, 4]) >>> Ab = np.array([1, 2, 3, 4], [5, 5, 5, 0], [2, 2, 0, 0]) >>> w = eigvals_banded(Ab, lower=True) >>> w array(-4.26200532, -2.22987175, 3.95222349, 12.53965359)

Solves a standard or generalized eigenvalue problem for a complex Hermitian or real symmetric matrix.

Find eigenvalues array ``w`` of array ``a``, where ``b`` is positive definite such that for every eigenvalue λ (i-th entry of w) and its eigenvector vi (i-th column of v) satisfies::

a @ vi = λ * b @ vi vi.conj().T @ a @ vi = λ vi.conj().T @ b @ vi = 1

In the standard problem, b is assumed to be the identity matrix.

Parameters ---------- a : (M, M) array_like A complex Hermitian or real symmetric matrix whose eigenvalues will be computed. b : (M, M) array_like, optional A complex Hermitian or real symmetric definite positive matrix in. If omitted, identity matrix is assumed. lower : bool, optional Whether the pertinent array data is taken from the lower or upper triangle of ``a`` and, if applicable, ``b``. (Default: lower) eigvals_only : bool, optional Whether to calculate only eigenvalues and no eigenvectors. (Default: both are calculated) subset_by_index : iterable, optional If provided, this two-element iterable defines the start and the end indices of the desired eigenvalues (ascending order and 0-indexed). To return only the second smallest to fifth smallest eigenvalues, ``1, 4`` is used. ``n-3, n-1`` returns the largest three. Only available with 'evr', 'evx', and 'gvx' drivers. The entries are directly converted to integers via ``int()``. subset_by_value : iterable, optional If provided, this two-element iterable defines the half-open interval ``(a, b]`` that, if any, only the eigenvalues between these values are returned. Only available with 'evr', 'evx', and 'gvx' drivers. Use ``np.inf`` for the unconstrained ends. driver: str, optional Defines which LAPACK driver should be used. Valid options are 'ev', 'evd', 'evr', 'evx' for standard problems and 'gv', 'gvd', 'gvx' for generalized (where b is not None) problems. See the Notes section of `scipy.linalg.eigh`. type : int, optional For the generalized problems, this keyword specifies the problem type to be solved for ``w`` and ``v`` (only takes 1, 2, 3 as possible inputs)::

1 => a @ v = w @ b @ v 2 => a @ b @ v = w @ v 3 => b @ a @ v = w @ v

This keyword is ignored for standard problems. overwrite_a : bool, optional Whether to overwrite data in ``a`` (may improve performance). Default is False. overwrite_b : bool, optional Whether to overwrite data in ``b`` (may improve performance). Default is False. check_finite : bool, optional Whether to check that the input matrices contain only finite numbers. Disabling may give a performance gain, but may result in problems (crashes, non-termination) if the inputs do contain infinities or NaNs. turbo : bool, optional *Deprecated by ``driver=gvd`` option*. Has no significant effect for eigenvalue computations since no eigenvectors are requested.

..Deprecated in v1.5.0 eigvals : tuple (lo, hi), optional *Deprecated by ``subset_by_index`` keyword*. Indexes of the smallest and largest (in ascending order) eigenvalues and corresponding eigenvectors to be returned: 0 <= lo <= hi <= M-1. If omitted, all eigenvalues and eigenvectors are returned.

.. Deprecated in v1.5.0

Returns ------- w : (N,) ndarray The ``N`` (``1<=N<=M``) selected eigenvalues, in ascending order, each repeated according to its multiplicity.

Raises ------ LinAlgError If eigenvalue computation does not converge, an error occurred, or b matrix is not definite positive. Note that if input matrices are not symmetric or Hermitian, no error will be reported but results will be wrong.

See Also -------- eigh : eigenvalues and right eigenvectors for symmetric/Hermitian arrays eigvals : eigenvalues of general arrays eigvals_banded : eigenvalues for symmetric/Hermitian band matrices eigvalsh_tridiagonal : eigenvalues of symmetric/Hermitian tridiagonal matrices

Notes ----- This function does not check the input array for being Hermitian/symmetric in order to allow for representing arrays with only their upper/lower triangular parts.

This function serves as a one-liner shorthand for `scipy.linalg.eigh` with the option ``eigvals_only=True`` to get the eigenvalues and not the eigenvectors. Here it is kept as a legacy convenience. It might be beneficial to use the main function to have full control and to be a bit more pythonic.

Examples -------- For more examples see `scipy.linalg.eigh`.

>>> from scipy.linalg import eigvalsh >>> A = np.array([6, 3, 1, 5], [3, 0, 5, 1], [1, 5, 6, 2], [5, 1, 2, 2]) >>> w = eigvalsh(A) >>> w array(-3.74637491, -0.76263923, 6.08502336, 12.42399079)

Solve eigenvalue problem for a real symmetric tridiagonal matrix.

Find eigenvalues `w` of ``a``::

a v:,i = wi v:,i v.H v = identity

For a real symmetric matrix ``a`` with diagonal elements `d` and off-diagonal elements `e`.

Parameters ---------- d : ndarray, shape (ndim,) The diagonal elements of the array. e : ndarray, shape (ndim-1,) The off-diagonal elements of the array. select : 'a', 'v', 'i', optional Which eigenvalues to calculate

====== ======================================== select calculated ====== ======================================== 'a' All eigenvalues 'v' Eigenvalues in the interval (min, max] 'i' Eigenvalues with indices min <= i <= max ====== ======================================== select_range : (min, max), optional Range of selected eigenvalues check_finite : bool, optional Whether to check that the input matrix contains only finite numbers. Disabling may give a performance gain, but may result in problems (crashes, non-termination) if the inputs do contain infinities or NaNs. tol : float The absolute tolerance to which each eigenvalue is required (only used when ``lapack_driver='stebz'``). An eigenvalue (or cluster) is considered to have converged if it lies in an interval of this width. If <= 0. (default), the value ``eps*|a|`` is used where eps is the machine precision, and ``|a|`` is the 1-norm of the matrix ``a``. lapack_driver : str LAPACK function to use, can be 'auto', 'stemr', 'stebz', 'sterf', or 'stev'. When 'auto' (default), it will use 'stemr' if ``select='a'`` and 'stebz' otherwise. 'sterf' and 'stev' can only be used when ``select='a'``.

Returns ------- w : (M,) ndarray The eigenvalues, in ascending order, each repeated according to its multiplicity.

Raises ------ LinAlgError If eigenvalue computation does not converge.

See Also -------- eigh_tridiagonal : eigenvalues and right eiegenvectors for symmetric/Hermitian tridiagonal matrices

Examples -------- >>> from scipy.linalg import eigvalsh_tridiagonal, eigvalsh >>> d = 3*np.ones(4) >>> e = -1*np.ones(3) >>> w = eigvalsh_tridiagonal(d, e) >>> A = np.diag(d) + np.diag(e, k=1) + np.diag(e, k=-1) >>> w2 = eigvalsh(A) # Verify with other eigenvalue routines >>> np.allclose(w - w2, np.zeros(4)) True

Compute the matrix exponential using Pade approximation.

Parameters ---------- A : (N, N) array_like or sparse matrix Matrix to be exponentiated.

Returns ------- expm : (N, N) ndarray Matrix exponential of `A`.

References ---------- .. 1 Awad H. Al-Mohy and Nicholas J. Higham (2009) 'A New Scaling and Squaring Algorithm for the Matrix Exponential.' SIAM Journal on Matrix Analysis and Applications. 31 (3). pp. 970-989. ISSN 1095-7162

Examples -------- >>> from scipy.linalg import expm, sinm, cosm

Matrix version of the formula exp(0) = 1:

>>> expm(np.zeros((2,2))) array([ 1., 0.], [ 0., 1.])

Euler's identity (exp(i*theta) = cos(theta) + i*sin(theta)) applied to a matrix:

>>> a = np.array([1.0, 2.0], [-1.0, 3.0]) >>> expm(1j*a) array([ 0.42645930+1.89217551j, -2.13721484-0.97811252j], [ 1.06860742+0.48905626j, -1.71075555+0.91406299j]) >>> cosm(a) + 1j*sinm(a) array([ 0.42645930+1.89217551j, -2.13721484-0.97811252j], [ 1.06860742+0.48905626j, -1.71075555+0.91406299j])

Relative condition number of the matrix exponential in the Frobenius norm.

Parameters ---------- A : 2-D array_like Square input matrix with shape (N, N). check_finite : bool, optional Whether to check that the input matrix contains only finite numbers. Disabling may give a performance gain, but may result in problems (crashes, non-termination) if the inputs do contain infinities or NaNs.

Returns ------- kappa : float The relative condition number of the matrix exponential in the Frobenius norm

Notes ----- A faster estimate for the condition number in the 1-norm has been published but is not yet implemented in SciPy.

.. versionadded:: 0.14.0

See also -------- expm : Compute the exponential of a matrix. expm_frechet : Compute the Frechet derivative of the matrix exponential.

Examples -------- >>> from scipy.linalg import expm_cond >>> A = np.array([-0.3, 0.2, 0.6], [0.6, 0.3, -0.1], [-0.7, 1.2, 0.9]) >>> k = expm_cond(A) >>> k 1.7787805864469866

Frechet derivative of the matrix exponential of A in the direction E.

Parameters ---------- A : (N, N) array_like Matrix of which to take the matrix exponential. E : (N, N) array_like Matrix direction in which to take the Frechet derivative. method : str, optional Choice of algorithm. Should be one of

• `SPS` (default)
• `blockEnlarge`

compute_expm : bool, optional Whether to compute also `expm_A` in addition to `expm_frechet_AE`. Default is True. check_finite : bool, optional Whether to check that the input matrix contains only finite numbers. Disabling may give a performance gain, but may result in problems (crashes, non-termination) if the inputs do contain infinities or NaNs.

Returns ------- expm_A : ndarray Matrix exponential of A. expm_frechet_AE : ndarray Frechet derivative of the matrix exponential of A in the direction E.

For ``compute_expm = False``, only `expm_frechet_AE` is returned.

See also -------- expm : Compute the exponential of a matrix.

Notes ----- This section describes the available implementations that can be selected by the `method` parameter. The default method is *SPS*.

Method *blockEnlarge* is a naive algorithm.

Method *SPS* is Scaling-Pade-Squaring 1_. It is a sophisticated implementation which should take only about 3/8 as much time as the naive implementation. The asymptotics are the same.

.. versionadded:: 0.13.0

References ---------- .. 1 Awad H. Al-Mohy and Nicholas J. Higham (2009) Computing the Frechet Derivative of the Matrix Exponential, with an application to Condition Number Estimation. SIAM Journal On Matrix Analysis and Applications., 30 (4). pp. 1639-1657. ISSN 1095-7162

Examples -------- >>> import scipy.linalg >>> A = np.random.randn(3, 3) >>> E = np.random.randn(3, 3) >>> expm_A, expm_frechet_AE = scipy.linalg.expm_frechet(A, E) >>> expm_A.shape, expm_frechet_AE.shape ((3, 3), (3, 3))

>>> import scipy.linalg >>> A = np.random.randn(3, 3) >>> E = np.random.randn(3, 3) >>> expm_A, expm_frechet_AE = scipy.linalg.expm_frechet(A, E) >>> M = np.zeros((6, 6)) >>> M:3, :3 = A; M:3, 3: = E; M3:, 3: = A >>> expm_M = scipy.linalg.expm(M) >>> np.allclose(expm_A, expm_M:3, :3) True >>> np.allclose(expm_frechet_AE, expm_M:3, 3:) True

Returns a symmetric Fiedler matrix

Given an sequence of numbers `a`, Fiedler matrices have the structure ``Fi, j = np.abs(ai - aj)``, and hence zero diagonals and nonnegative entries. A Fiedler matrix has a dominant positive eigenvalue and other eigenvalues are negative. Although not valid generally, for certain inputs, the inverse and the determinant can be derived explicitly as given in 1_.

Parameters ---------- a : (n,) array_like coefficient array

Returns ------- F : (n, n) ndarray

See Also -------- circulant, toeplitz

Notes -----

.. versionadded:: 1.3.0

References ---------- .. 1 J. Todd, 'Basic Numerical Mathematics: Vol.2 : Numerical Algebra', 1977, Birkhauser, :doi:`10.1007/978-3-0348-7286-7`

Examples -------- >>> from scipy.linalg import det, inv, fiedler >>> a = 1, 4, 12, 45, 77 >>> n = len(a) >>> A = fiedler(a) >>> A array([ 0, 3, 11, 44, 76], [ 3, 0, 8, 41, 73], [11, 8, 0, 33, 65], [44, 41, 33, 0, 32], [76, 73, 65, 32, 0])

The explicit formulas for determinant and inverse seem to hold only for monotonically increasing/decreasing arrays. Note the tridiagonal structure and the corners.

>>> Ai = inv(A) >>> Ainp.abs(Ai) < 1e-12 = 0. # cleanup the numerical noise for display >>> Ai array([-0.16008772, 0.16666667, 0. , 0. , 0.00657895], [ 0.16666667, -0.22916667, 0.0625 , 0. , 0. ], [ 0. , 0.0625 , -0.07765152, 0.01515152, 0. ], [ 0. , 0. , 0.01515152, -0.03077652, 0.015625 ], [ 0.00657895, 0. , 0. , 0.015625 , -0.00904605]) >>> det(A) 15409151.999999998 >>> (-1)**(n-1) * 2**(n-2) * np.diff(a).prod() * (a-1 - a0) 15409152

Returns a Fiedler companion matrix

Given a polynomial coefficient array ``a``, this function forms a pentadiagonal matrix with a special structure whose eigenvalues coincides with the roots of ``a``.

Parameters ---------- a : (N,) array_like 1-D array of polynomial coefficients in descending order with a nonzero leading coefficient. For ``N < 2``, an empty array is returned.

Returns ------- c : (N-1, N-1) ndarray Resulting companion matrix

Notes ----- Similar to `companion` the leading coefficient should be nonzero. In the case the leading coefficient is not 1, other coefficients are rescaled before the array generation. To avoid numerical issues, it is best to provide a monic polynomial.

.. versionadded:: 1.3.0

See Also -------- companion

References ---------- .. 1 M. Fiedler, ' A note on companion matrices', Linear Algebra and its Applications, 2003, :doi:`10.1016/S0024-3795(03)00548-2`

Examples -------- >>> from scipy.linalg import fiedler_companion, eigvals >>> p = np.poly(np.arange(1, 9, 2)) # 1., -16., 86., -176., 105. >>> fc = fiedler_companion(p) >>> fc array([ 16., -86., 1., 0.], [ 1., 0., 0., 0.], [ 0., 176., 0., -105.], [ 0., 1., 0., 0.]) >>> eigvals(fc) array(7.+0.j, 5.+0.j, 3.+0.j, 1.+0.j)

Find best-matching BLAS/LAPACK type.

Arrays are used to determine the optimal prefix of BLAS routines.

Parameters ---------- arrays : sequence of ndarrays, optional Arrays can be given to determine optimal prefix of BLAS routines. If not given, double-precision routines will be used, otherwise the most generic type in arrays will be used. dtype : str or dtype, optional Data-type specifier. Not used if `arrays` is non-empty.

Returns ------- prefix : str BLAS/LAPACK prefix character. dtype : dtype Inferred Numpy data type. prefer_fortran : bool Whether to prefer Fortran order routines over C order.

Examples -------- >>> import scipy.linalg.blas as bla >>> a = np.random.rand(10,15) >>> b = np.asfortranarray(a) # Change the memory layout order >>> bla.find_best_blas_type((a,)) ('d', dtype('float64'), False) >>> bla.find_best_blas_type((a*1j,)) ('z', dtype('complex128'), False) >>> bla.find_best_blas_type((b,)) ('d', dtype('float64'), True)

Compute the fractional power of a matrix.

Proceeds according to the discussion in section (6) of 1_.

Parameters ---------- A : (N, N) array_like Matrix whose fractional power to evaluate. t : float Fractional power.

Returns ------- X : (N, N) array_like The fractional power of the matrix.

References ---------- .. 1 Nicholas J. Higham and Lijing lin (2011) 'A Schur-Pade Algorithm for Fractional Powers of a Matrix.' SIAM Journal on Matrix Analysis and Applications, 32 (3). pp. 1056-1078. ISSN 0895-4798

Examples -------- >>> from scipy.linalg import fractional_matrix_power >>> a = np.array([1.0, 3.0], [1.0, 4.0]) >>> b = fractional_matrix_power(a, 0.5) >>> b array([ 0.75592895, 1.13389342], [ 0.37796447, 1.88982237]) >>> np.dot(b, b) # Verify square root array([ 1., 3.], [ 1., 4.])

Evaluate a matrix function specified by a callable.

Returns the value of matrix-valued function ``f`` at `A`. The function ``f`` is an extension of the scalar-valued function `func` to matrices.

Parameters ---------- A : (N, N) array_like Matrix at which to evaluate the function func : callable Callable object that evaluates a scalar function f. Must be vectorized (eg. using vectorize). disp : bool, optional Print warning if error in the result is estimated large instead of returning estimated error. (Default: True)

Returns ------- funm : (N, N) ndarray Value of the matrix function specified by func evaluated at `A` errest : float (if disp == False)

1-norm of the estimated error, ||err||_1 / ||A||_1

Examples -------- >>> from scipy.linalg import funm >>> a = np.array([1.0, 3.0], [1.0, 4.0]) >>> funm(a, lambda x: x*x) array([ 4., 15.], [ 5., 19.]) >>> a.dot(a) array([ 4., 15.], [ 5., 19.])

Notes ----- This function implements the general algorithm based on Schur decomposition (Algorithm 9.1.1. in 1_).

If the input matrix is known to be diagonalizable, then relying on the eigendecomposition is likely to be faster. For example, if your matrix is Hermitian, you can do

>>> from scipy.linalg import eigh >>> def funm_herm(a, func, check_finite=False): ... w, v = eigh(a, check_finite=check_finite) ... ## if you further know that your matrix is positive semidefinite, ... ## you can optionally guard against precision errors by doing ... # w = np.maximum(w, 0) ... w = func(w) ... return (v * w).dot(v.conj().T)

References ---------- .. 1 Gene H. Golub, Charles F. van Loan, Matrix Computations 4th ed.

Return available BLAS function objects from names.

Arrays are used to determine the optimal prefix of BLAS routines.

Parameters ---------- names : str or sequence of str Name(s) of BLAS functions without type prefix.

arrays : sequence of ndarrays, optional Arrays can be given to determine optimal prefix of BLAS routines. If not given, double-precision routines will be used, otherwise the most generic type in arrays will be used.

dtype : str or dtype, optional Data-type specifier. Not used if `arrays` is non-empty.

Returns ------- funcs : list List containing the found function(s).

Notes ----- This routine automatically chooses between Fortran/C interfaces. Fortran code is used whenever possible for arrays with column major order. In all other cases, C code is preferred.

In BLAS, the naming convention is that all functions start with a type prefix, which depends on the type of the principal matrix. These can be one of 's', 'd', 'c', 'z' for the NumPy types float32, float64, complex64, complex128 respectively. The code and the dtype are stored in attributes `typecode` and `dtype` of the returned functions.

Examples -------- >>> import scipy.linalg as LA >>> a = np.random.rand(3,2) >>> x_gemv = LA.get_blas_funcs('gemv', (a,)) >>> x_gemv.typecode 'd' >>> x_gemv = LA.get_blas_funcs('gemv',(a*1j,)) >>> x_gemv.typecode 'z'

Return available LAPACK function objects from names.

Arrays are used to determine the optimal prefix of LAPACK routines.

Parameters ---------- names : str or sequence of str Name(s) of LAPACK functions without type prefix.

arrays : sequence of ndarrays, optional Arrays can be given to determine optimal prefix of LAPACK routines. If not given, double-precision routines will be used, otherwise the most generic type in arrays will be used.

dtype : str or dtype, optional Data-type specifier. Not used if `arrays` is non-empty.

Returns ------- funcs : list List containing the found function(s).

Notes ----- This routine automatically chooses between Fortran/C interfaces. Fortran code is used whenever possible for arrays with column major order. In all other cases, C code is preferred.

In LAPACK, the naming convention is that all functions start with a type prefix, which depends on the type of the principal matrix. These can be one of 's', 'd', 'c', 'z' for the NumPy types float32, float64, complex64, complex128 respectively, and are stored in attribute ``typecode`` of the returned functions.

Examples -------- Suppose we would like to use '?lange' routine which computes the selected norm of an array. We pass our array in order to get the correct 'lange' flavor.

>>> import scipy.linalg as LA >>> a = np.random.rand(3,2) >>> x_lange = LA.get_lapack_funcs('lange', (a,)) >>> x_lange.typecode 'd' >>> x_lange = LA.get_lapack_funcs('lange',(a*1j,)) >>> x_lange.typecode 'z'

Several LAPACK routines work best when its internal WORK array has the optimal size (big enough for fast computation and small enough to avoid waste of memory). This size is determined also by a dedicated query to the function which is often wrapped as a standalone function and commonly denoted as ``###_lwork``. Below is an example for ``?sysv``

>>> import scipy.linalg as LA >>> a = np.random.rand(1000,1000) >>> b = np.random.rand(1000,1)*1j >>> # We pick up zsysv and zsysv_lwork due to b array ... xsysv, xlwork = LA.get_lapack_funcs(('sysv', 'sysv_lwork'), (a, b)) >>> opt_lwork, _ = xlwork(a.shape0) # returns a complex for 'z' prefix >>> udut, ipiv, x, info = xsysv(a, b, lwork=int(opt_lwork.real))

Construct an Hadamard matrix.

Constructs an n-by-n Hadamard matrix, using Sylvester's construction. `n` must be a power of 2.

Parameters ---------- n : int The order of the matrix. `n` must be a power of 2. dtype : dtype, optional The data type of the array to be constructed.

Returns ------- H : (n, n) ndarray The Hadamard matrix.

Notes ----- .. versionadded:: 0.8.0

Examples -------- >>> from scipy.linalg import hadamard >>> hadamard(2, dtype=complex) array([ 1.+0.j, 1.+0.j], [ 1.+0.j, -1.-0.j]) >>> hadamard(4) array([ 1, 1, 1, 1], [ 1, -1, 1, -1], [ 1, 1, -1, -1], [ 1, -1, -1, 1])

Construct a Hankel matrix.

The Hankel matrix has constant anti-diagonals, with `c` as its first column and `r` as its last row. If `r` is not given, then `r = zeros_like(c)` is assumed.

Parameters ---------- c : array_like First column of the matrix. Whatever the actual shape of `c`, it will be converted to a 1-D array. r : array_like, optional Last row of the matrix. If None, ``r = zeros_like(c)`` is assumed. r0 is ignored; the last row of the returned matrix is ``c[-1], r[1:]``. Whatever the actual shape of `r`, it will be converted to a 1-D array.

Returns ------- A : (len(c), len(r)) ndarray The Hankel matrix. Dtype is the same as ``(c0 + r0).dtype``.

See Also -------- toeplitz : Toeplitz matrix circulant : circulant matrix

Examples -------- >>> from scipy.linalg import hankel >>> hankel(1, 17, 99) array([ 1, 17, 99], [17, 99, 0], [99, 0, 0]) >>> hankel(1,2,3,4, 4,7,7,8,9) array([1, 2, 3, 4, 7], [2, 3, 4, 7, 7], [3, 4, 7, 7, 8], [4, 7, 7, 8, 9])

Create an Helmert matrix of order `n`.

This has applications in statistics, compositional or simplicial analysis, and in Aitchison geometry.

Parameters ---------- n : int The size of the array to create. full : bool, optional If True the (n, n) ndarray will be returned. Otherwise the submatrix that does not include the first row will be returned. Default: False.

Returns ------- M : ndarray The Helmert matrix. The shape is (n, n) or (n-1, n) depending on the `full` argument.

Examples -------- >>> from scipy.linalg import helmert >>> helmert(5, full=True) array([ 0.4472136 , 0.4472136 , 0.4472136 , 0.4472136 , 0.4472136 ], [ 0.70710678, -0.70710678, 0. , 0. , 0. ], [ 0.40824829, 0.40824829, -0.81649658, 0. , 0. ], [ 0.28867513, 0.28867513, 0.28867513, -0.8660254 , 0. ], [ 0.2236068 , 0.2236068 , 0.2236068 , 0.2236068 , -0.89442719])

Compute Hessenberg form of a matrix.

The Hessenberg decomposition is::

A = Q H Q^H

where `Q` is unitary/orthogonal and `H` has only zero elements below the first sub-diagonal.

Parameters ---------- a : (M, M) array_like Matrix to bring into Hessenberg form. calc_q : bool, optional Whether to compute the transformation matrix. Default is False. overwrite_a : bool, optional Whether to overwrite `a`; may improve performance. Default is False. check_finite : bool, optional Whether to check that the input matrix contains only finite numbers. Disabling may give a performance gain, but may result in problems (crashes, non-termination) if the inputs do contain infinities or NaNs.

Returns ------- H : (M, M) ndarray Hessenberg form of `a`. Q : (M, M) ndarray Unitary/orthogonal similarity transformation matrix ``A = Q H Q^H``. Only returned if ``calc_q=True``.

Examples -------- >>> from scipy.linalg import hessenberg >>> A = np.array([2, 5, 8, 7], [5, 2, 2, 8], [7, 5, 6, 6], [5, 4, 4, 8]) >>> H, Q = hessenberg(A, calc_q=True) >>> H array([ 2. , -11.65843866, 1.42005301, 0.25349066], [ -9.94987437, 14.53535354, -5.31022304, 2.43081618], [ 0. , -1.83299243, 0.38969961, -0.51527034], [ 0. , 0. , -3.83189513, 1.07494686]) >>> np.allclose(Q @ H @ Q.conj().T - A, np.zeros((4, 4))) True

Create a Hilbert matrix of order `n`.

Returns the `n` by `n` array with entries `hi,j = 1 / (i + j + 1)`.

Parameters ---------- n : int The size of the array to create.

Returns ------- h : (n, n) ndarray The Hilbert matrix.

See Also -------- invhilbert : Compute the inverse of a Hilbert matrix.

Notes ----- .. versionadded:: 0.10.0

Examples -------- >>> from scipy.linalg import hilbert >>> hilbert(3) array([ 1. , 0.5 , 0.33333333], [ 0.5 , 0.33333333, 0.25 ], [ 0.33333333, 0.25 , 0.2 ])

Compute the inverse of a matrix.

Parameters ---------- a : array_like Square matrix to be inverted. overwrite_a : bool, optional Discard data in `a` (may improve performance). Default is False. check_finite : bool, optional Whether to check that the input matrix contains only finite numbers. Disabling may give a performance gain, but may result in problems (crashes, non-termination) if the inputs do contain infinities or NaNs.

Returns ------- ainv : ndarray Inverse of the matrix `a`.

Raises ------ LinAlgError If `a` is singular. ValueError If `a` is not square, or not 2D.

Examples -------- >>> from scipy import linalg >>> a = np.array([1., 2.], [3., 4.]) >>> linalg.inv(a) array([-2. , 1. ], [ 1.5, -0.5]) >>> np.dot(a, linalg.inv(a)) array([ 1., 0.], [ 0., 1.])

Compute the inverse of the Hilbert matrix of order `n`.

The entries in the inverse of a Hilbert matrix are integers. When `n` is greater than 14, some entries in the inverse exceed the upper limit of 64 bit integers. The `exact` argument provides two options for dealing with these large integers.

Parameters ---------- n : int The order of the Hilbert matrix. exact : bool, optional If False, the data type of the array that is returned is np.float64, and the array is an approximation of the inverse. If True, the array is the exact integer inverse array. To represent the exact inverse when n > 14, the returned array is an object array of long integers. For n <= 14, the exact inverse is returned as an array with data type np.int64.

Returns ------- invh : (n, n) ndarray The data type of the array is np.float64 if `exact` is False. If `exact` is True, the data type is either np.int64 (for n <= 14) or object (for n > 14). In the latter case, the objects in the array will be long integers.

See Also -------- hilbert : Create a Hilbert matrix.

Notes ----- .. versionadded:: 0.10.0

Examples -------- >>> from scipy.linalg import invhilbert >>> invhilbert(4) array([ 16., -120., 240., -140.], [ -120., 1200., -2700., 1680.], [ 240., -2700., 6480., -4200.], [ -140., 1680., -4200., 2800.]) >>> invhilbert(4, exact=True) array([ 16, -120, 240, -140], [ -120, 1200, -2700, 1680], [ 240, -2700, 6480, -4200], [ -140, 1680, -4200, 2800], dtype=int64) >>> invhilbert(16)7,7 4.2475099528537506e+19 >>> invhilbert(16, exact=True)7,7 42475099528537378560

Returns the inverse of the n x n Pascal matrix.

The Pascal matrix is a matrix containing the binomial coefficients as its elements.

Parameters ---------- n : int The size of the matrix to create; that is, the result is an n x n matrix. kind : str, optional Must be one of 'symmetric', 'lower', or 'upper'. Default is 'symmetric'. exact : bool, optional If `exact` is True, the result is either an array of type ``numpy.int64`` (if `n` <= 35) or an object array of Python integers. If `exact` is False, the coefficients in the matrix are computed using `scipy.special.comb` with `exact=False`. The result will be a floating point array, and for large `n`, the values in the array will not be the exact coefficients.

Returns ------- invp : (n, n) ndarray The inverse of the Pascal matrix.

See Also -------- pascal

Notes -----

.. versionadded:: 0.16.0

References ---------- .. 1 'Pascal matrix', https://en.wikipedia.org/wiki/Pascal_matrix .. 2 Cohen, A. M., 'The inverse of a Pascal matrix', Mathematical Gazette, 59(408), pp. 111-112, 1975.

Examples -------- >>> from scipy.linalg import invpascal, pascal >>> invp = invpascal(5) >>> invp array([ 5, -10, 10, -5, 1], [-10, 30, -35, 19, -4], [ 10, -35, 46, -27, 6], [ -5, 19, -27, 17, -4], [ 1, -4, 6, -4, 1])

>>> p = pascal(5) >>> p.dot(invp) array([ 1., 0., 0., 0., 0.], [ 0., 1., 0., 0., 0.], [ 0., 0., 1., 0., 0.], [ 0., 0., 0., 1., 0.], [ 0., 0., 0., 0., 1.])

An example of the use of `kind` and `exact`:

>>> invpascal(5, kind='lower', exact=False) array([ 1., -0., 0., -0., 0.], [-1., 1., -0., 0., -0.], [ 1., -2., 1., -0., 0.], [-1., 3., -3., 1., -0.], [ 1., -4., 6., -4., 1.])

Khatri-rao product

A column-wise Kronecker product of two matrices

Parameters ---------- a: (n, k) array_like Input array b: (m, k) array_like Input array

Returns ------- c: (n*m, k) ndarray Khatri-rao product of `a` and `b`.

Notes ----- The mathematical definition of the Khatri-Rao product is:

.. math::

(A_j \bigotimes B_j)_j

which is the Kronecker product of every column of A and B, e.g.::

c = np.vstack(np.kron(a[:, k], b[:, k]) for k in range(b.shape[1])).T

See Also -------- kron : Kronecker product

Examples -------- >>> from scipy import linalg >>> a = np.array([1, 2, 3], [4, 5, 6]) >>> b = np.array([3, 4, 5], [6, 7, 8], [2, 3, 9]) >>> linalg.khatri_rao(a, b) array([ 3, 8, 15], [ 6, 14, 24], [ 2, 6, 27], [12, 20, 30], [24, 35, 48], [ 8, 15, 54])

Kronecker product.

The result is the block matrix::

a0,0*b a0,1*b ... a0,-1*b a1,0*b a1,1*b ... a1,-1*b ... a-1,0*b a-1,1*b ... a-1,-1*b

Parameters ---------- a : (M, N) ndarray Input array b : (P, Q) ndarray Input array

Returns ------- A : (M*P, N*Q) ndarray Kronecker product of `a` and `b`.

Examples -------- >>> from numpy import array >>> from scipy.linalg import kron >>> kron(array([1,2],[3,4]), array([1,1,1])) array([1, 1, 1, 2, 2, 2], [3, 3, 3, 4, 4, 4])

Computes the LDLt or Bunch-Kaufman factorization of a symmetric/ hermitian matrix.

This function returns a block diagonal matrix D consisting blocks of size at most 2x2 and also a possibly permuted unit lower triangular matrix ``L`` such that the factorization ``A = L D L^H`` or ``A = L D L^T`` holds. If ``lower`` is False then (again possibly permuted) upper triangular matrices are returned as outer factors.

The permutation array can be used to triangularize the outer factors simply by a row shuffle, i.e., ``luperm, :`` is an upper/lower triangular matrix. This is also equivalent to multiplication with a permutation matrix ``P.dot(lu)``, where ``P`` is a column-permuted identity matrix ``I:, perm``.

Depending on the value of the boolean ``lower``, only upper or lower triangular part of the input array is referenced. Hence, a triangular matrix on entry would give the same result as if the full matrix is supplied.

Parameters ---------- a : array_like Square input array lower : bool, optional This switches between the lower and upper triangular outer factors of the factorization. Lower triangular (``lower=True``) is the default. hermitian : bool, optional For complex-valued arrays, this defines whether ``a = a.conj().T`` or ``a = a.T`` is assumed. For real-valued arrays, this switch has no effect. overwrite_a : bool, optional Allow overwriting data in ``a`` (may enhance performance). The default is False. check_finite : bool, optional Whether to check that the input matrices contain only finite numbers. Disabling may give a performance gain, but may result in problems (crashes, non-termination) if the inputs do contain infinities or NaNs.

Returns ------- lu : ndarray The (possibly) permuted upper/lower triangular outer factor of the factorization. d : ndarray The block diagonal multiplier of the factorization. perm : ndarray The row-permutation index array that brings lu into triangular form.

Raises ------ ValueError If input array is not square. ComplexWarning If a complex-valued array with nonzero imaginary parts on the diagonal is given and hermitian is set to True.

Examples -------- Given an upper triangular array `a` that represents the full symmetric array with its entries, obtain `l`, 'd' and the permutation vector `perm`:

>>> import numpy as np >>> from scipy.linalg import ldl >>> a = np.array([2, -1, 3], [0, 2, 0], [0, 0, 1]) >>> lu, d, perm = ldl(a, lower=0) # Use the upper part >>> lu array([ 0. , 0. , 1. ], [ 0. , 1. , -0.5], [ 1. , 1. , 1.5]) >>> d array([-5. , 0. , 0. ], [ 0. , 1.5, 0. ], [ 0. , 0. , 2. ]) >>> perm array(2, 1, 0) >>> luperm, : array([ 1. , 1. , 1.5], [ 0. , 1. , -0.5], [ 0. , 0. , 1. ]) >>> lu.dot(d).dot(lu.T) array([ 2., -1., 3.], [-1., 2., 0.], [ 3., 0., 1.])

Notes ----- This function uses ``?SYTRF`` routines for symmetric matrices and ``?HETRF`` routines for Hermitian matrices from LAPACK. See 1_ for the algorithm details.

Depending on the ``lower`` keyword value, only lower or upper triangular part of the input array is referenced. Moreover, this keyword also defines the structure of the outer factors of the factorization.

.. versionadded:: 1.1.0

See also -------- cholesky, lu

References ---------- .. 1 J.R. Bunch, L. Kaufman, Some stable methods for calculating inertia and solving symmetric linear systems, Math. Comput. Vol.31, 1977. DOI: 10.2307/2005787

Create a Leslie matrix.

Given the length n array of fecundity coefficients `f` and the length n-1 array of survival coefficients `s`, return the associated Leslie matrix.

Parameters ---------- f : (N,) array_like The 'fecundity' coefficients. s : (N-1,) array_like The 'survival' coefficients, has to be 1-D. The length of `s` must be one less than the length of `f`, and it must be at least 1.

Returns ------- L : (N, N) ndarray The array is zero except for the first row, which is `f`, and the first sub-diagonal, which is `s`. The data-type of the array will be the data-type of ``f0+s0``.

Notes ----- .. versionadded:: 0.8.0

The Leslie matrix is used to model discrete-time, age-structured population growth 1_ 2_. In a population with `n` age classes, two sets of parameters define a Leslie matrix: the `n` 'fecundity coefficients', which give the number of offspring per-capita produced by each age class, and the `n` - 1 'survival coefficients', which give the per-capita survival rate of each age class.

References ---------- .. 1 P. H. Leslie, On the use of matrices in certain population mathematics, Biometrika, Vol. 33, No. 3, 183--212 (Nov. 1945) .. 2 P. H. Leslie, Some further notes on the use of matrices in population mathematics, Biometrika, Vol. 35, No. 3/4, 213--245 (Dec. 1948)

Examples -------- >>> from scipy.linalg import leslie >>> leslie(0.1, 2.0, 1.0, 0.1, 0.2, 0.8, 0.7) array([ 0.1, 2. , 1. , 0.1], [ 0.2, 0. , 0. , 0. ], [ 0. , 0.8, 0. , 0. ], [ 0. , 0. , 0.7, 0. ])

Compute matrix logarithm.

The matrix logarithm is the inverse of expm: expm(logm(`A`)) == `A`

Parameters ---------- A : (N, N) array_like Matrix whose logarithm to evaluate disp : bool, optional Print warning if error in the result is estimated large instead of returning estimated error. (Default: True)

Returns ------- logm : (N, N) ndarray Matrix logarithm of `A` errest : float (if disp == False)

1-norm of the estimated error, ||err||_1 / ||A||_1

References ---------- .. 1 Awad H. Al-Mohy and Nicholas J. Higham (2012) 'Improved Inverse Scaling and Squaring Algorithms for the Matrix Logarithm.' SIAM Journal on Scientific Computing, 34 (4). C152-C169. ISSN 1095-7197

.. 2 Nicholas J. Higham (2008) 'Functions of Matrices: Theory and Computation' ISBN 978-0-898716-46-7

.. 3 Nicholas J. Higham and Lijing lin (2011) 'A Schur-Pade Algorithm for Fractional Powers of a Matrix.' SIAM Journal on Matrix Analysis and Applications, 32 (3). pp. 1056-1078. ISSN 0895-4798

Examples -------- >>> from scipy.linalg import logm, expm >>> a = np.array([1.0, 3.0], [1.0, 4.0]) >>> b = logm(a) >>> b array([-1.02571087, 2.05142174], [ 0.68380725, 1.02571087]) >>> expm(b) # Verify expm(logm(a)) returns a array([ 1., 3.], [ 1., 4.])

Compute least-squares solution to equation Ax = b.

Compute a vector x such that the 2-norm ``|b - A x|`` is minimized.

Parameters ---------- a : (M, N) array_like Left-hand side array b : (M,) or (M, K) array_like Right hand side array cond : float, optional Cutoff for 'small' singular values; used to determine effective rank of a. Singular values smaller than ``rcond * largest_singular_value`` are considered zero. overwrite_a : bool, optional Discard data in `a` (may enhance performance). Default is False. overwrite_b : bool, optional Discard data in `b` (may enhance performance). Default is False. check_finite : bool, optional Whether to check that the input matrices contain only finite numbers. Disabling may give a performance gain, but may result in problems (crashes, non-termination) if the inputs do contain infinities or NaNs. lapack_driver : str, optional Which LAPACK driver is used to solve the least-squares problem. Options are ``'gelsd'``, ``'gelsy'``, ``'gelss'``. Default (``'gelsd'``) is a good choice. However, ``'gelsy'`` can be slightly faster on many problems. ``'gelss'`` was used historically. It is generally slow but uses less memory.

.. versionadded:: 0.17.0

Returns ------- x : (N,) or (N, K) ndarray Least-squares solution. Return shape matches shape of `b`. residues : (K,) ndarray or float Square of the 2-norm for each column in ``b - a x``, if ``M > N`` and ``ndim(A) == n`` (returns a scalar if b is 1-D). Otherwise a (0,)-shaped array is returned. rank : int Effective rank of `a`. s : (min(M, N),) ndarray or None Singular values of `a`. The condition number of a is ``abs(s0 / s-1)``.

Raises ------ LinAlgError If computation does not converge.

ValueError When parameters are not compatible.

See Also -------- scipy.optimize.nnls : linear least squares with non-negativity constraint

Notes ----- When ``'gelsy'`` is used as a driver, `residues` is set to a (0,)-shaped array and `s` is always ``None``.

Examples -------- >>> from scipy.linalg import lstsq >>> import matplotlib.pyplot as plt

Suppose we have the following data:

>>> x = np.array(1, 2.5, 3.5, 4, 5, 7, 8.5) >>> y = np.array(0.3, 1.1, 1.5, 2.0, 3.2, 6.6, 8.6)

We want to fit a quadratic polynomial of the form ``y = a + b*x**2`` to this data. We first form the 'design matrix' M, with a constant column of 1s and a column containing ``x**2``:

>>> M = x:, np.newaxis**0, 2 >>> M array([ 1. , 1. ], [ 1. , 6.25], [ 1. , 12.25], [ 1. , 16. ], [ 1. , 25. ], [ 1. , 49. ], [ 1. , 72.25])

We want to find the least-squares solution to ``M.dot(p) = y``, where ``p`` is a vector with length 2 that holds the parameters ``a`` and ``b``.

>>> p, res, rnk, s = lstsq(M, y) >>> p array( 0.20925829, 0.12013861)

Plot the data and the fitted curve.

>>> plt.plot(x, y, 'o', label='data') >>> xx = np.linspace(0, 9, 101) >>> yy = p0 + p1*xx**2 >>> plt.plot(xx, yy, label='least squares fit, $y = a + bx^2$') >>> plt.xlabel('x') >>> plt.ylabel('y') >>> plt.legend(framealpha=1, shadow=True) >>> plt.grid(alpha=0.25) >>> plt.show()

Compute pivoted LU decomposition of a matrix.

The decomposition is::

A = P L U

where P is a permutation matrix, L lower triangular with unit diagonal elements, and U upper triangular.

Parameters ---------- a : (M, N) array_like Array to decompose permute_l : bool, optional Perform the multiplication P*L (Default: do not permute) overwrite_a : bool, optional Whether to overwrite data in a (may improve performance) check_finite : bool, optional Whether to check that the input matrix contains only finite numbers. Disabling may give a performance gain, but may result in problems (crashes, non-termination) if the inputs do contain infinities or NaNs.

Returns ------- **(If permute_l == False)**

p : (M, M) ndarray Permutation matrix l : (M, K) ndarray Lower triangular or trapezoidal matrix with unit diagonal. K = min(M, N) u : (K, N) ndarray Upper triangular or trapezoidal matrix

**(If permute_l == True)**

pl : (M, K) ndarray Permuted L matrix. K = min(M, N) u : (K, N) ndarray Upper triangular or trapezoidal matrix

Notes ----- This is a LU factorization routine written for SciPy.

Examples -------- >>> from scipy.linalg import lu >>> A = np.array([2, 5, 8, 7], [5, 2, 2, 8], [7, 5, 6, 6], [5, 4, 4, 8]) >>> p, l, u = lu(A) >>> np.allclose(A - p @ l @ u, np.zeros((4, 4))) True

Compute pivoted LU decomposition of a matrix.

The decomposition is::

A = P L U

where P is a permutation matrix, L lower triangular with unit diagonal elements, and U upper triangular.

Parameters ---------- a : (M, M) array_like Matrix to decompose overwrite_a : bool, optional Whether to overwrite data in A (may increase performance) check_finite : bool, optional Whether to check that the input matrix contains only finite numbers. Disabling may give a performance gain, but may result in problems (crashes, non-termination) if the inputs do contain infinities or NaNs.

Returns ------- lu : (N, N) ndarray Matrix containing U in its upper triangle, and L in its lower triangle. The unit diagonal elements of L are not stored. piv : (N,) ndarray Pivot indices representing the permutation matrix P: row i of matrix was interchanged with row pivi.

See also -------- lu_solve : solve an equation system using the LU factorization of a matrix

Notes ----- This is a wrapper to the ``*GETRF`` routines from LAPACK.

Examples -------- >>> from scipy.linalg import lu_factor >>> A = np.array([2, 5, 8, 7], [5, 2, 2, 8], [7, 5, 6, 6], [5, 4, 4, 8]) >>> lu, piv = lu_factor(A) >>> piv array(2, 2, 3, 3, dtype=int32)

Convert LAPACK's ``piv`` array to NumPy index and test the permutation

>>> piv_py = 2, 0, 3, 1 >>> L, U = np.tril(lu, k=-1) + np.eye(4), np.triu(lu) >>> np.allclose(Apiv_py - L @ U, np.zeros((4, 4))) True

Solve an equation system, a x = b, given the LU factorization of a

Parameters ---------- (lu, piv) Factorization of the coefficient matrix a, as given by lu_factor b : array Right-hand side trans :

, 1, 2

, optional Type of system to solve:

===== ========= trans system ===== ========= 0 a x = b 1 a^T x = b 2 a^H x = b ===== ========= overwrite_b : bool, optional Whether to overwrite data in b (may increase performance) check_finite : bool, optional Whether to check that the input matrices contain only finite numbers. Disabling may give a performance gain, but may result in problems (crashes, non-termination) if the inputs do contain infinities or NaNs.

Returns ------- x : array Solution to the system

See also -------- lu_factor : LU factorize a matrix

Examples -------- >>> from scipy.linalg import lu_factor, lu_solve >>> A = np.array([2, 5, 8, 7], [5, 2, 2, 8], [7, 5, 6, 6], [5, 4, 4, 8]) >>> b = np.array(1, 1, 1, 1) >>> lu, piv = lu_factor(A) >>> x = lu_solve((lu, piv), b) >>> np.allclose(A @ x - b, np.zeros((4,))) True

Compute a diagonal similarity transformation for row/column balancing.

The balancing tries to equalize the row and column 1-norms by applying a similarity transformation such that the magnitude variation of the matrix entries is reflected to the scaling matrices.

Moreover, if enabled, the matrix is first permuted to isolate the upper triangular parts of the matrix and, again if scaling is also enabled, only the remaining subblocks are subjected to scaling.

The balanced matrix satisfies the following equality

.. math::

B = T^

1

}

A T

The scaling coefficients are approximated to the nearest power of 2 to avoid round-off errors.

Parameters ---------- A : (n, n) array_like Square data matrix for the balancing. permute : bool, optional The selector to define whether permutation of A is also performed prior to scaling. scale : bool, optional The selector to turn on and off the scaling. If False, the matrix will not be scaled. separate : bool, optional This switches from returning a full matrix of the transformation to a tuple of two separate 1-D permutation and scaling arrays. overwrite_a : bool, optional This is passed to xGEBAL directly. Essentially, overwrites the result to the data. It might increase the space efficiency. See LAPACK manual for details. This is False by default.

Returns ------- B : (n, n) ndarray Balanced matrix T : (n, n) ndarray A possibly permuted diagonal matrix whose nonzero entries are integer powers of 2 to avoid numerical truncation errors. scale, perm : (n,) ndarray If ``separate`` keyword is set to True then instead of the array ``T`` above, the scaling and the permutation vectors are given separately as a tuple without allocating the full array ``T``.

Notes -----

This algorithm is particularly useful for eigenvalue and matrix decompositions and in many cases it is already called by various LAPACK routines.

The algorithm is based on the well-known technique of 1_ and has been modified to account for special cases. See 2_ for details which have been implemented since LAPACK v3.5.0. Before this version there are corner cases where balancing can actually worsen the conditioning. See 3_ for such examples.

The code is a wrapper around LAPACK's xGEBAL routine family for matrix balancing.

.. versionadded:: 0.19.0

Examples -------- >>> from scipy import linalg >>> x = np.array([1,2,0], [9,1,0.01], [1,2,10*np.pi])

>>> y, permscale = linalg.matrix_balance(x) >>> np.abs(x).sum(axis=0) / np.abs(x).sum(axis=1) array( 3.66666667, 0.4995005 , 0.91312162)

>>> np.abs(y).sum(axis=0) / np.abs(y).sum(axis=1) array( 1.2 , 1.27041742, 0.92658316) # may vary

>>> permscale # only powers of 2 (0.5 == 2^(-1)) array([ 0.5, 0. , 0. ], # may vary [ 0. , 1. , 0. ], [ 0. , 0. , 1. ])

References ---------- .. 1 : B.N. Parlett and C. Reinsch, 'Balancing a Matrix for Calculation of Eigenvalues and Eigenvectors', Numerische Mathematik, Vol.13(4), 1969, DOI:10.1007/BF02165404

.. 2 : R. James, J. Langou, B.R. Lowery, 'On matrix balancing and eigenvector computation', 2014, Available online: https://arxiv.org/abs/1401.5766

.. 3 : D.S. Watkins. A case where balancing is harmful. Electron. Trans. Numer. Anal, Vol.23, 2006.

Matrix or vector norm.

This function is able to return one of seven different matrix norms, or one of an infinite number of vector norms (described below), depending on the value of the ``ord`` parameter.

Parameters ---------- a : (M,) or (M, N) array_like Input array. If `axis` is None, `a` must be 1D or 2D. ord : non-zero int, inf, -inf, 'fro', optional Order of the norm (see table under ``Notes``). inf means NumPy's `inf` object axis : nt, 2-tuple of ints, None, optional If `axis` is an integer, it specifies the axis of `a` along which to compute the vector norms. If `axis` is a 2-tuple, it specifies the axes that hold 2-D matrices, and the matrix norms of these matrices are computed. If `axis` is None then either a vector norm (when `a` is 1-D) or a matrix norm (when `a` is 2-D) is returned. keepdims : bool, optional If this is set to True, the axes which are normed over are left in the result as dimensions with size one. With this option the result will broadcast correctly against the original `a`. check_finite : bool, optional Whether to check that the input matrix contains only finite numbers. Disabling may give a performance gain, but may result in problems (crashes, non-termination) if the inputs do contain infinities or NaNs.

Returns ------- n : float or ndarray Norm of the matrix or vector(s).

Notes ----- For values of ``ord <= 0``, the result is, strictly speaking, not a mathematical 'norm', but it may still be useful for various numerical purposes.

The following norms can be calculated:

===== ============================ ========================== ord norm for matrices norm for vectors ===== ============================ ========================== None Frobenius norm 2-norm 'fro' Frobenius norm -- inf max(sum(abs(x), axis=1)) max(abs(x)) -inf min(sum(abs(x), axis=1)) min(abs(x)) 0 -- sum(x != 0) 1 max(sum(abs(x), axis=0)) as below -1 min(sum(abs(x), axis=0)) as below 2 2-norm (largest sing. value) as below -2 smallest singular value as below other -- sum(abs(x)**ord)**(1./ord) ===== ============================ ==========================

The Frobenius norm is given by 1_:

:math:`||A||_F = \sum_{i,j} abs(a_{i,j})^2^

/2

`

The ``axis`` and ``keepdims`` arguments are passed directly to ``numpy.linalg.norm`` and are only usable if they are supported by the version of numpy in use.

References ---------- .. 1 G. H. Golub and C. F. Van Loan, *Matrix Computations*, Baltimore, MD, Johns Hopkins University Press, 1985, pg. 15

Examples -------- >>> from scipy.linalg import norm >>> a = np.arange(9) - 4.0 >>> a array(-4., -3., -2., -1., 0., 1., 2., 3., 4.) >>> b = a.reshape((3, 3)) >>> b array([-4., -3., -2.], [-1., 0., 1.], [ 2., 3., 4.])

>>> norm(a) 7.745966692414834 >>> norm(b) 7.745966692414834 >>> norm(b, 'fro') 7.745966692414834 >>> norm(a, np.inf) 4 >>> norm(b, np.inf) 9 >>> norm(a, -np.inf) 0 >>> norm(b, -np.inf) 2

>>> norm(a, 1) 20 >>> norm(b, 1) 7 >>> norm(a, -1) -4.6566128774142013e-010 >>> norm(b, -1) 6 >>> norm(a, 2) 7.745966692414834 >>> norm(b, 2) 7.3484692283495345

>>> norm(a, -2) 0 >>> norm(b, -2) 1.8570331885190563e-016 >>> norm(a, 3) 5.8480354764257312 >>> norm(a, -3) 0

Construct an orthonormal basis for the null space of A using SVD

Parameters ---------- A : (M, N) array_like Input array rcond : float, optional Relative condition number. Singular values ``s`` smaller than ``rcond * max(s)`` are considered zero. Default: floating point eps * max(M,N).

Returns ------- Z : (N, K) ndarray Orthonormal basis for the null space of A. K = dimension of effective null space, as determined by rcond

See also -------- svd : Singular value decomposition of a matrix orth : Matrix range

Examples -------- 1-D null space:

>>> from scipy.linalg import null_space >>> A = np.array([1, 1], [1, 1]) >>> ns = null_space(A) >>> ns * np.sign(ns0,0) # Remove the sign ambiguity of the vector array([ 0.70710678], [-0.70710678])

2-D null space:

>>> B = np.random.rand(3, 5) >>> Z = null_space(B) >>> Z.shape (5, 2) >>> np.allclose(B.dot(Z), 0) True

The basis vectors are orthonormal (up to rounding error):

>>> Z.T.dot(Z) array([ 1.00000000e+00, 6.92087741e-17], [ 6.92087741e-17, 1.00000000e+00])

QZ decomposition for a pair of matrices with reordering.

.. versionadded:: 0.17.0

Parameters ---------- A : (N, N) array_like 2-D array to decompose B : (N, N) array_like 2-D array to decompose sort : callable, 'lhp', 'rhp', 'iuc', 'ouc', optional Specifies whether the upper eigenvalues should be sorted. A callable may be passed that, given an ordered pair ``(alpha, beta)`` representing the eigenvalue ``x = (alpha/beta)``, returns a boolean denoting whether the eigenvalue should be sorted to the top-left (True). For the real matrix pairs ``beta`` is real while ``alpha`` can be complex, and for complex matrix pairs both ``alpha`` and ``beta`` can be complex. The callable must be able to accept a NumPy array. Alternatively, string parameters may be used:

• 'lhp' Left-hand plane (x.real < 0.0)
• 'rhp' Right-hand plane (x.real > 0.0)
• 'iuc' Inside the unit circle (x*x.conjugate() < 1.0)
• 'ouc' Outside the unit circle (x*x.conjugate() > 1.0)

With the predefined sorting functions, an infinite eigenvalue (i.e., ``alpha != 0`` and ``beta = 0``) is considered to lie in neither the left-hand nor the right-hand plane, but it is considered to lie outside the unit circle. For the eigenvalue ``(alpha, beta) = (0, 0)``, the predefined sorting functions all return `False`. output : str 'real','complex', optional Construct the real or complex QZ decomposition for real matrices. Default is 'real'. overwrite_a : bool, optional If True, the contents of A are overwritten. overwrite_b : bool, optional If True, the contents of B are overwritten. check_finite : bool, optional If true checks the elements of `A` and `B` are finite numbers. If false does no checking and passes matrix through to underlying algorithm.

Returns ------- AA : (N, N) ndarray Generalized Schur form of A. BB : (N, N) ndarray Generalized Schur form of B. alpha : (N,) ndarray alpha = alphar + alphai * 1j. See notes. beta : (N,) ndarray See notes. Q : (N, N) ndarray The left Schur vectors. Z : (N, N) ndarray The right Schur vectors.

Notes ----- On exit, ``(ALPHAR(j) + ALPHAI(j)*i)/BETA(j), j=1,...,N``, will be the generalized eigenvalues. ``ALPHAR(j) + ALPHAI(j)*i`` and ``BETA(j),j=1,...,N`` are the diagonals of the complex Schur form (S,T) that would result if the 2-by-2 diagonal blocks of the real generalized Schur form of (A,B) were further reduced to triangular form using complex unitary transformations. If ALPHAI(j) is zero, then the jth eigenvalue is real; if positive, then the ``j``th and ``(j+1)``st eigenvalues are a complex conjugate pair, with ``ALPHAI(j+1)`` negative.

See also -------- qz

Examples -------- >>> from scipy.linalg import ordqz >>> A = np.array([2, 5, 8, 7], [5, 2, 2, 8], [7, 5, 6, 6], [5, 4, 4, 8]) >>> B = np.array([0, 6, 0, 0], [5, 0, 2, 1], [5, 2, 6, 6], [4, 7, 7, 7]) >>> AA, BB, alpha, beta, Q, Z = ordqz(A, B, sort='lhp')

Since we have sorted for left half plane eigenvalues, negatives come first

>>> (alpha/beta).real < 0 array( True, True, False, False, dtype=bool)

Construct an orthonormal basis for the range of A using SVD

Parameters ---------- A : (M, N) array_like Input array rcond : float, optional Relative condition number. Singular values ``s`` smaller than ``rcond * max(s)`` are considered zero. Default: floating point eps * max(M,N).

Returns ------- Q : (M, K) ndarray Orthonormal basis for the range of A. K = effective rank of A, as determined by rcond

See also -------- svd : Singular value decomposition of a matrix null_space : Matrix null space

Examples -------- >>> from scipy.linalg import orth >>> A = np.array([2, 0, 0], [0, 5, 0]) # rank 2 array >>> orth(A) array([0., 1.], [1., 0.]) >>> orth(A.T) array([0., 1.], [1., 0.], [0., 0.])

Compute the matrix solution of the orthogonal Procrustes problem.

Given matrices A and B of equal shape, find an orthogonal matrix R that most closely maps A to B using the algorithm given in 1_.

Parameters ---------- A : (M, N) array_like Matrix to be mapped. B : (M, N) array_like Target matrix. check_finite : bool, optional Whether to check that the input matrices contain only finite numbers. Disabling may give a performance gain, but may result in problems (crashes, non-termination) if the inputs do contain infinities or NaNs.

Returns ------- R : (N, N) ndarray The matrix solution of the orthogonal Procrustes problem. Minimizes the Frobenius norm of ``(A @ R) - B``, subject to ``R.T @ R = I``. scale : float Sum of the singular values of ``A.T @ B``.

Raises ------ ValueError If the input array shapes don't match or if check_finite is True and the arrays contain Inf or NaN.

Notes ----- Note that unlike higher level Procrustes analyses of spatial data, this function only uses orthogonal transformations like rotations and reflections, and it does not use scaling or translation.

.. versionadded:: 0.15.0

References ---------- .. 1 Peter H. Schonemann, 'A generalized solution of the orthogonal Procrustes problem', Psychometrica -- Vol. 31, No. 1, March, 1996.

Examples -------- >>> from scipy.linalg import orthogonal_procrustes >>> A = np.array([ 2, 0, 1], [-2, 0, 0])

Flip the order of columns and check for the anti-diagonal mapping

>>> R, sca = orthogonal_procrustes(A, np.fliplr(A)) >>> R array([-5.34384992e-17, 0.00000000e+00, 1.00000000e+00], [ 0.00000000e+00, 1.00000000e+00, 0.00000000e+00], [ 1.00000000e+00, 0.00000000e+00, -7.85941422e-17]) >>> sca 9.0

Returns the n x n Pascal matrix.

The Pascal matrix is a matrix containing the binomial coefficients as its elements.

Parameters ---------- n : int The size of the matrix to create; that is, the result is an n x n matrix. kind : str, optional Must be one of 'symmetric', 'lower', or 'upper'. Default is 'symmetric'. exact : bool, optional If `exact` is True, the result is either an array of type numpy.uint64 (if n < 35) or an object array of Python long integers. If `exact` is False, the coefficients in the matrix are computed using `scipy.special.comb` with `exact=False`. The result will be a floating point array, and the values in the array will not be the exact coefficients, but this version is much faster than `exact=True`.

Returns ------- p : (n, n) ndarray The Pascal matrix.

See Also -------- invpascal

Notes ----- See https://en.wikipedia.org/wiki/Pascal_matrix for more information about Pascal matrices.

.. versionadded:: 0.11.0

Examples -------- >>> from scipy.linalg import pascal >>> pascal(4) array([ 1, 1, 1, 1], [ 1, 2, 3, 4], [ 1, 3, 6, 10], [ 1, 4, 10, 20], dtype=uint64) >>> pascal(4, kind='lower') array([1, 0, 0, 0], [1, 1, 0, 0], [1, 2, 1, 0], [1, 3, 3, 1], dtype=uint64) >>> pascal(50)-1, -1 25477612258980856902730428600 >>> from scipy.special import comb >>> comb(98, 49, exact=True) 25477612258980856902730428600

Compute the (Moore-Penrose) pseudo-inverse of a matrix.

Calculate a generalized inverse of a matrix using a least-squares solver.

Parameters ---------- a : (M, N) array_like Matrix to be pseudo-inverted. cond, rcond : float, optional Cutoff factor for 'small' singular values. In `lstsq`, singular values less than ``cond*largest_singular_value`` will be considered as zero. If both are omitted, the default value ``max(M, N) * eps`` is passed to `lstsq` where ``eps`` is the corresponding machine precision value of the datatype of ``a``.

.. versionchanged:: 1.3.0 Previously the default cutoff value was just `eps` without the factor ``max(M, N)``.

return_rank : bool, optional if True, return the effective rank of the matrix check_finite : bool, optional Whether to check that the input matrix contains only finite numbers. Disabling may give a performance gain, but may result in problems (crashes, non-termination) if the inputs do contain infinities or NaNs.

Returns ------- B : (N, M) ndarray The pseudo-inverse of matrix `a`. rank : int The effective rank of the matrix. Returned if return_rank == True

Raises ------ LinAlgError If computation does not converge.

Examples -------- >>> from scipy import linalg >>> a = np.random.randn(9, 6) >>> B = linalg.pinv(a) >>> np.allclose(a, np.dot(a, np.dot(B, a))) True >>> np.allclose(B, np.dot(B, np.dot(a, B))) True

Compute the (Moore-Penrose) pseudo-inverse of a matrix.

Calculate a generalized inverse of a matrix using its singular-value decomposition and including all 'large' singular values.

Parameters ---------- a : (M, N) array_like Matrix to be pseudo-inverted. cond, rcond : float or None Cutoff for 'small' singular values; singular values smaller than this value are considered as zero. If both are omitted, the default value ``max(M,N)*largest_singular_value*eps`` is used where ``eps`` is the machine precision value of the datatype of ``a``.

.. versionchanged:: 1.3.0 Previously the default cutoff value was just ``eps*f`` where ``f`` was ``1e3`` for single precision and ``1e6`` for double precision.

return_rank : bool, optional If True, return the effective rank of the matrix. check_finite : bool, optional Whether to check that the input matrix contains only finite numbers. Disabling may give a performance gain, but may result in problems (crashes, non-termination) if the inputs do contain infinities or NaNs.

Returns ------- B : (N, M) ndarray The pseudo-inverse of matrix `a`. rank : int The effective rank of the matrix. Returned if `return_rank` is True.

Raises ------ LinAlgError If SVD computation does not converge.

Examples -------- >>> from scipy import linalg >>> a = np.random.randn(9, 6) >>> B = linalg.pinv2(a) >>> np.allclose(a, np.dot(a, np.dot(B, a))) True >>> np.allclose(B, np.dot(B, np.dot(a, B))) True

Compute the (Moore-Penrose) pseudo-inverse of a Hermitian matrix.

Calculate a generalized inverse of a Hermitian or real symmetric matrix using its eigenvalue decomposition and including all eigenvalues with 'large' absolute value.

Parameters ---------- a : (N, N) array_like Real symmetric or complex hermetian matrix to be pseudo-inverted cond, rcond : float or None Cutoff for 'small' singular values; singular values smaller than this value are considered as zero. If both are omitted, the default ``max(M,N)*largest_eigenvalue*eps`` is used where ``eps`` is the machine precision value of the datatype of ``a``.

.. versionchanged:: 1.3.0 Previously the default cutoff value was just ``eps*f`` where ``f`` was ``1e3`` for single precision and ``1e6`` for double precision.

lower : bool, optional Whether the pertinent array data is taken from the lower or upper triangle of `a`. (Default: lower) return_rank : bool, optional If True, return the effective rank of the matrix. check_finite : bool, optional Whether to check that the input matrix contains only finite numbers. Disabling may give a performance gain, but may result in problems (crashes, non-termination) if the inputs do contain infinities or NaNs.

Returns ------- B : (N, N) ndarray The pseudo-inverse of matrix `a`. rank : int The effective rank of the matrix. Returned if `return_rank` is True.

Raises ------ LinAlgError If eigenvalue does not converge

Examples -------- >>> from scipy.linalg import pinvh >>> a = np.random.randn(9, 6) >>> a = np.dot(a, a.T) >>> B = pinvh(a) >>> np.allclose(a, np.dot(a, np.dot(B, a))) True >>> np.allclose(B, np.dot(B, np.dot(a, B))) True

Compute the polar decomposition.

Returns the factors of the polar decomposition 1_ `u` and `p` such that ``a = up`` (if `side` is 'right') or ``a = pu`` (if `side` is 'left'), where `p` is positive semidefinite. Depending on the shape of `a`, either the rows or columns of `u` are orthonormal. When `a` is a square array, `u` is a square unitary array. When `a` is not square, the 'canonical polar decomposition' 2_ is computed.

Parameters ---------- a : (m, n) array_like The array to be factored. side : 'left', 'right', optional Determines whether a right or left polar decomposition is computed. If `side` is 'right', then ``a = up``. If `side` is 'left', then ``a = pu``. The default is 'right'.

Returns ------- u : (m, n) ndarray If `a` is square, then `u` is unitary. If m > n, then the columns of `a` are orthonormal, and if m < n, then the rows of `u` are orthonormal. p : ndarray `p` is Hermitian positive semidefinite. If `a` is nonsingular, `p` is positive definite. The shape of `p` is (n, n) or (m, m), depending on whether `side` is 'right' or 'left', respectively.

References ---------- .. 1 R. A. Horn and C. R. Johnson, 'Matrix Analysis', Cambridge University Press, 1985. .. 2 N. J. Higham, 'Functions of Matrices: Theory and Computation', SIAM, 2008.

Examples -------- >>> from scipy.linalg import polar >>> a = np.array([1, -1], [2, 4]) >>> u, p = polar(a) >>> u array([ 0.85749293, -0.51449576], [ 0.51449576, 0.85749293]) >>> p array([ 1.88648444, 1.2004901 ], [ 1.2004901 , 3.94446746])

A non-square example, with m < n:

>>> b = np.array([0.5, 1, 2], [1.5, 3, 4]) >>> u, p = polar(b) >>> u array([-0.21196618, -0.42393237, 0.88054056], [ 0.39378971, 0.78757942, 0.4739708 ]) >>> p array([ 0.48470147, 0.96940295, 1.15122648], [ 0.96940295, 1.9388059 , 2.30245295], [ 1.15122648, 2.30245295, 3.65696431]) >>> u.dot(p) # Verify the decomposition. array([ 0.5, 1. , 2. ], [ 1.5, 3. , 4. ]) >>> u.dot(u.T) # The rows of u are orthonormal. array([ 1.00000000e+00, -2.07353665e-17], [ -2.07353665e-17, 1.00000000e+00])

Another non-square example, with m > n:

>>> c = b.T >>> u, p = polar(c) >>> u array([-0.21196618, 0.39378971], [-0.42393237, 0.78757942], [ 0.88054056, 0.4739708 ]) >>> p array([ 1.23116567, 1.93241587], [ 1.93241587, 4.84930602]) >>> u.dot(p) # Verify the decomposition. array([ 0.5, 1.5], [ 1. , 3. ], [ 2. , 4. ]) >>> u.T.dot(u) # The columns of u are orthonormal. array([ 1.00000000e+00, -1.26363763e-16], [ -1.26363763e-16, 1.00000000e+00])

Compute QR decomposition of a matrix.

Calculate the decomposition ``A = Q R`` where Q is unitary/orthogonal and R upper triangular.

Parameters ---------- a : (M, N) array_like Matrix to be decomposed overwrite_a : bool, optional Whether data in `a` is overwritten (may improve performance if `overwrite_a` is set to True by reusing the existing input data structure rather than creating a new one.) lwork : int, optional Work array size, lwork >= a.shape1. If None or -1, an optimal size is computed. mode : 'full', 'r', 'economic', 'raw', optional Determines what information is to be returned: either both Q and R ('full', default), only R ('r') or both Q and R but computed in economy-size ('economic', see Notes). The final option 'raw' (added in SciPy 0.11) makes the function return two matrices (Q, TAU) in the internal format used by LAPACK. pivoting : bool, optional Whether or not factorization should include pivoting for rank-revealing qr decomposition. If pivoting, compute the decomposition ``A P = Q R`` as above, but where P is chosen such that the diagonal of R is non-increasing. check_finite : bool, optional Whether to check that the input matrix contains only finite numbers. Disabling may give a performance gain, but may result in problems (crashes, non-termination) if the inputs do contain infinities or NaNs.

Returns ------- Q : float or complex ndarray Of shape (M, M), or (M, K) for ``mode='economic'``. Not returned if ``mode='r'``. R : float or complex ndarray Of shape (M, N), or (K, N) for ``mode='economic'``. ``K = min(M, N)``. P : int ndarray Of shape (N,) for ``pivoting=True``. Not returned if ``pivoting=False``.

Raises ------ LinAlgError Raised if decomposition fails

Notes ----- This is an interface to the LAPACK routines dgeqrf, zgeqrf, dorgqr, zungqr, dgeqp3, and zgeqp3.

If ``mode=economic``, the shapes of Q and R are (M, K) and (K, N) instead of (M,M) and (M,N), with ``K=min(M,N)``.

Examples -------- >>> from scipy import linalg >>> a = np.random.randn(9, 6)

>>> q, r = linalg.qr(a) >>> np.allclose(a, np.dot(q, r)) True >>> q.shape, r.shape ((9, 9), (9, 6))

>>> r2 = linalg.qr(a, mode='r') >>> np.allclose(r, r2) True

>>> q3, r3 = linalg.qr(a, mode='economic') >>> q3.shape, r3.shape ((9, 6), (6, 6))

>>> q4, r4, p4 = linalg.qr(a, pivoting=True) >>> d = np.abs(np.diag(r4)) >>> np.all(d1: <= d:-1) True >>> np.allclose(a:, p4, np.dot(q4, r4)) True >>> q4.shape, r4.shape, p4.shape ((9, 9), (9, 6), (6,))

>>> q5, r5, p5 = linalg.qr(a, mode='economic', pivoting=True) >>> q5.shape, r5.shape, p5.shape ((9, 6), (6, 6), (6,))

qr_insert(Q, R, u, k, which=u'row', rcond=None, overwrite_qru=False, check_finite=True)

QR update on row or column insertions

If ``A = Q R`` is the QR factorization of ``A``, return the QR factorization of ``A`` where rows or columns have been inserted starting at row or column ``k``.

Parameters ---------- Q : (M, M) array_like Unitary/orthogonal matrix from the QR decomposition of A. R : (M, N) array_like Upper triangular matrix from the QR decomposition of A. u : (N,), (p, N), (M,), or (M, p) array_like Rows or columns to insert k : int Index before which `u` is to be inserted. which: 'row', 'col', optional Determines if rows or columns will be inserted, defaults to 'row' rcond : float Lower bound on the reciprocal condition number of ``Q`` augmented with ``u/||u||`` Only used when updating economic mode (thin, (M,N) (N,N)) decompositions. If None, machine precision is used. Defaults to None. overwrite_qru : bool, optional If True, consume Q, R, and u, if possible, while performing the update, otherwise make copies as necessary. Defaults to False. check_finite : bool, optional Whether to check that the input matrices contain only finite numbers. Disabling may give a performance gain, but may result in problems (crashes, non-termination) if the inputs do contain infinities or NaNs. Default is True.

Returns ------- Q1 : ndarray Updated unitary/orthogonal factor R1 : ndarray Updated upper triangular factor

Raises ------ LinAlgError : If updating a (M,N) (N,N) factorization and the reciprocal condition number of Q augmented with u/||u|| is smaller than rcond.

See Also -------- qr, qr_multiply, qr_delete, qr_update

Notes ----- This routine does not guarantee that the diagonal entries of ``R1`` are positive.

.. versionadded:: 0.16.0

References ----------

.. 1 Golub, G. H. & Van Loan, C. F. Matrix Computations, 3rd Ed. (Johns Hopkins University Press, 1996).

.. 2 Daniel, J. W., Gragg, W. B., Kaufman, L. & Stewart, G. W. Reorthogonalization and stable algorithms for updating the Gram-Schmidt QR factorization. Math. Comput. 30, 772-795 (1976).

.. 3 Reichel, L. & Gragg, W. B. Algorithm 686: FORTRAN Subroutines for Updating the QR Decomposition. ACM Trans. Math. Softw. 16, 369-377 (1990).

Examples -------- >>> from scipy import linalg >>> a = np.array([ 3., -2., -2.], ... [ 6., -7., 4.], ... [ 7., 8., -6.]) >>> q, r = linalg.qr(a)

Given this QR decomposition, update q and r when 2 rows are inserted.

>>> u = np.array([ 6., -9., -3.], ... [ -3., 10., 1.]) >>> q1, r1 = linalg.qr_insert(q, r, u, 2, 'row') >>> q1 array([-0.25445668, 0.02246245, 0.18146236, -0.72798806, 0.60979671], # may vary (signs) [-0.50891336, 0.23226178, -0.82836478, -0.02837033, -0.00828114], [-0.50891336, 0.35715302, 0.38937158, 0.58110733, 0.35235345], [ 0.25445668, -0.52202743, -0.32165498, 0.36263239, 0.65404509], [-0.59373225, -0.73856549, 0.16065817, -0.0063658 , -0.27595554]) >>> r1 array([-11.78982612, 6.44623587, 3.81685018], # may vary (signs) [ 0. , -16.01393278, 3.72202865], [ 0. , 0. , -6.13010256], [ 0. , 0. , 0. ], [ 0. , 0. , 0. ])

The update is equivalent, but faster than the following.

>>> a1 = np.insert(a, 2, u, 0) >>> a1 array([ 3., -2., -2.], [ 6., -7., 4.], [ 6., -9., -3.], [ -3., 10., 1.], [ 7., 8., -6.]) >>> q_direct, r_direct = linalg.qr(a1)

Check that we have equivalent results:

>>> np.dot(q1, r1) array([ 3., -2., -2.], [ 6., -7., 4.], [ 6., -9., -3.], [ -3., 10., 1.], [ 7., 8., -6.])

>>> np.allclose(np.dot(q1, r1), a1) True

And the updated Q is still unitary:

>>> np.allclose(np.dot(q1.T, q1), np.eye(5)) True

Calculate the QR decomposition and multiply Q with a matrix.

Calculate the decomposition ``A = Q R`` where Q is unitary/orthogonal and R upper triangular. Multiply Q with a vector or a matrix c.

Parameters ---------- a : (M, N), array_like Input array c : array_like Input array to be multiplied by ``q``. mode : 'left', 'right', optional ``Q @ c`` is returned if mode is 'left', ``c @ Q`` is returned if mode is 'right'. The shape of c must be appropriate for the matrix multiplications, if mode is 'left', ``min(a.shape) == c.shape0``, if mode is 'right', ``a.shape0 == c.shape1``. pivoting : bool, optional Whether or not factorization should include pivoting for rank-revealing qr decomposition, see the documentation of qr. conjugate : bool, optional Whether Q should be complex-conjugated. This might be faster than explicit conjugation. overwrite_a : bool, optional Whether data in a is overwritten (may improve performance) overwrite_c : bool, optional Whether data in c is overwritten (may improve performance). If this is used, c must be big enough to keep the result, i.e. ``c.shape0`` = ``a.shape0`` if mode is 'left'.

Returns ------- CQ : ndarray The product of ``Q`` and ``c``. R : (K, N), ndarray R array of the resulting QR factorization where ``K = min(M, N)``. P : (N,) ndarray Integer pivot array. Only returned when ``pivoting=True``.

Raises ------ LinAlgError Raised if QR decomposition fails.

Notes ----- This is an interface to the LAPACK routines ``?GEQRF``, ``?ORMQR``, ``?UNMQR``, and ``?GEQP3``.

.. versionadded:: 0.11.0

Examples -------- >>> from scipy.linalg import qr_multiply, qr >>> A = np.array([1, 3, 3], [2, 3, 2], [2, 3, 3], [1, 3, 2]) >>> qc, r1, piv1 = qr_multiply(A, 2*np.eye(4), pivoting=1) >>> qc array([-1., 1., -1.], [-1., -1., 1.], [-1., -1., -1.], [-1., 1., 1.]) >>> r1 array([-6., -3., -5. ], [ 0., -1., -1.11022302e-16], [ 0., 0., -1. ]) >>> piv1 array(1, 0, 2, dtype=int32) >>> q2, r2, piv2 = qr(A, mode='economic', pivoting=1) >>> np.allclose(2*q2 - qc, np.zeros((4, 3))) True

qr_update(Q, R, u, v, overwrite_qruv=False, check_finite=True)

Rank-k QR update

If ``A = Q R`` is the QR factorization of ``A``, return the QR factorization of ``A + u v**T`` for real ``A`` or ``A + u v**H`` for complex ``A``.

Parameters ---------- Q : (M, M) or (M, N) array_like Unitary/orthogonal matrix from the qr decomposition of A. R : (M, N) or (N, N) array_like Upper triangular matrix from the qr decomposition of A. u : (M,) or (M, k) array_like Left update vector v : (N,) or (N, k) array_like Right update vector overwrite_qruv : bool, optional If True, consume Q, R, u, and v, if possible, while performing the update, otherwise make copies as necessary. Defaults to False. check_finite : bool, optional Whether to check that the input matrix contains only finite numbers. Disabling may give a performance gain, but may result in problems (crashes, non-termination) if the inputs do contain infinities or NaNs. Default is True.

Returns ------- Q1 : ndarray Updated unitary/orthogonal factor R1 : ndarray Updated upper triangular factor

See Also -------- qr, qr_multiply, qr_delete, qr_insert

Notes ----- This routine does not guarantee that the diagonal entries of `R1` are real or positive.

.. versionadded:: 0.16.0

References ---------- .. 1 Golub, G. H. & Van Loan, C. F. Matrix Computations, 3rd Ed. (Johns Hopkins University Press, 1996).

.. 2 Daniel, J. W., Gragg, W. B., Kaufman, L. & Stewart, G. W. Reorthogonalization and stable algorithms for updating the Gram-Schmidt QR factorization. Math. Comput. 30, 772-795 (1976).

.. 3 Reichel, L. & Gragg, W. B. Algorithm 686: FORTRAN Subroutines for Updating the QR Decomposition. ACM Trans. Math. Softw. 16, 369-377 (1990).

Examples -------- >>> from scipy import linalg >>> a = np.array([ 3., -2., -2.], ... [ 6., -9., -3.], ... [ -3., 10., 1.], ... [ 6., -7., 4.], ... [ 7., 8., -6.]) >>> q, r = linalg.qr(a)

Given this q, r decomposition, perform a rank 1 update.

>>> u = np.array(7., -2., 4., 3., 5.) >>> v = np.array(1., 3., -5.) >>> q_up, r_up = linalg.qr_update(q, r, u, v, False) >>> q_up array([ 0.54073807, 0.18645997, 0.81707661, -0.02136616, 0.06902409], # may vary (signs) [ 0.21629523, -0.63257324, 0.06567893, 0.34125904, -0.65749222], [ 0.05407381, 0.64757787, -0.12781284, -0.20031219, -0.72198188], [ 0.48666426, -0.30466718, -0.27487277, -0.77079214, 0.0256951 ], [ 0.64888568, 0.23001 , -0.4859845 , 0.49883891, 0.20253783]) >>> r_up array([ 18.49324201, 24.11691794, -44.98940746], # may vary (signs) [ 0. , 31.95894662, -27.40998201], [ 0. , 0. , -9.25451794], [ 0. , 0. , 0. ], [ 0. , 0. , 0. ])

The update is equivalent, but faster than the following.

>>> a_up = a + np.outer(u, v) >>> q_direct, r_direct = linalg.qr(a_up)

Check that we have equivalent results:

>>> np.allclose(np.dot(q_up, r_up), a_up) True

And the updated Q is still unitary:

>>> np.allclose(np.dot(q_up.T, q_up), np.eye(5)) True

Updating economic (reduced, thin) decompositions is also possible:

>>> qe, re = linalg.qr(a, mode='economic') >>> qe_up, re_up = linalg.qr_update(qe, re, u, v, False) >>> qe_up array([ 0.54073807, 0.18645997, 0.81707661], # may vary (signs) [ 0.21629523, -0.63257324, 0.06567893], [ 0.05407381, 0.64757787, -0.12781284], [ 0.48666426, -0.30466718, -0.27487277], [ 0.64888568, 0.23001 , -0.4859845 ]) >>> re_up array([ 18.49324201, 24.11691794, -44.98940746], # may vary (signs) [ 0. , 31.95894662, -27.40998201], [ 0. , 0. , -9.25451794]) >>> np.allclose(np.dot(qe_up, re_up), a_up) True >>> np.allclose(np.dot(qe_up.T, qe_up), np.eye(3)) True

Similarly to the above, perform a rank 2 update.

>>> u2 = np.array([ 7., -1,], ... [-2., 4.], ... [ 4., 2.], ... [ 3., -6.], ... [ 5., 3.]) >>> v2 = np.array([ 1., 2.], ... [ 3., 4.], ... [-5., 2]) >>> q_up2, r_up2 = linalg.qr_update(q, r, u2, v2, False) >>> q_up2 array([-0.33626508, -0.03477253, 0.61956287, -0.64352987, -0.29618884], # may vary (signs) [-0.50439762, 0.58319694, -0.43010077, -0.33395279, 0.33008064], [-0.21016568, -0.63123106, 0.0582249 , -0.13675572, 0.73163206], [ 0.12609941, 0.49694436, 0.64590024, 0.31191919, 0.47187344], [-0.75659643, -0.11517748, 0.10284903, 0.5986227 , -0.21299983]) >>> r_up2 array([-23.79075451, -41.1084062 , 24.71548348], # may vary (signs) [ 0. , -33.83931057, 11.02226551], [ 0. , 0. , 48.91476811], [ 0. , 0. , 0. ], [ 0. , 0. , 0. ])

This update is also a valid qr decomposition of ``A + U V**T``.

>>> a_up2 = a + np.dot(u2, v2.T) >>> np.allclose(a_up2, np.dot(q_up2, r_up2)) True >>> np.allclose(np.dot(q_up2.T, q_up2), np.eye(5)) True